Cell-free DNA for assessing and/or treating cancer

ABSTRACT

This document relates to methods and materials for assessed, monitored, and/or treated mammals (e.g., humans) having cancer. For example, methods and materials for identifying a mammal as having cancer (e.g., a localized cancer) are provided. For example, methods and materials for assessing, monitoring, and/or treating a mammal having cancer are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of International Application No. PCT/US2019/032914, filed May 17, 2019, which claims the benefit of U.S. Patent Application Ser. No. 62/673,516, filed on May 18, 2018, and claims the benefit of U.S. Patent Application Ser. No. 62/795,900, filed on Jan. 23, 2019. The disclosure of the prior applications are considered part of (and are incorporated by reference in) the disclosure of this application.

STATEMENT REGARDING FEDERAL FUNDING

This invention was made with U.S. government support under grant No. CA121113 from the National Institutes of Health. The U.S. government has certain rights in the invention.

BACKGROUND 1. Technical Field

This document relates to methods and materials for assessing and/or treating mammals (e.g., humans) having cancer. For example, this document provides methods and materials for identifying a mammal as having cancer (e.g., a localized cancer). For example, this document provides methods and materials for monitoring and/or treating a mammal having cancer.

2. Background Information

Much of the morbidity and mortality of human cancers world-wide is a result of the late diagnosis of these diseases, where treatments are less effective (Torre et al., 2015 CA Cancer J Clin 65:87; and World Health Organization, 2017 Guide to Cancer Early Diagnosis). Unfortunately, clinically proven biomarkers that can be used to broadly diagnose and treat patients are not widely available (Mazzucchelli, 2000 Advances in clinical pathology 4:111; Ruibal Morell, 1992 The International journal of biological markers 7:160; Galli et al., 2013 Clinical chemistry and laboratory medicine 51:1369; Sikaris, 2011 Heart, lung & circulation 20:634; Lin et al., 2016 in Screening for Colorectal Cancer: A Systematic Review for the U.S. Preventive Services Task Force. (Rockville, Md.); Wanebo et al., 1978 N Engl J Med 299:448; and Zauber, 2015 Dig Dis Sci 60:681).

SUMMARY

Recent analyses of cell-free DNA suggests that such approaches may provide new avenues for early diagnosis (Phallen et al., 2017 Sci Transl Med 9; Cohen et al., 2018 Science 359:926; Alix-Panabieres et al., 2016 Cancer discovery 6:479; Siravegna et al., 2017 Nature reviews. Clinical oncology 14:531; Haber et al., 2014 Cancer discovery 4:650; Husain et al., 2017 JAMA 318:1272; and Wan et al., 2017 Nat Rev Cancer 17:223).

This document provides methods and materials for determining a cell free DNA (cfDNA) fragmentation profile in a mammal (e.g., in a sample obtained from a mammal). In some cases, determining a cfDNA fragmentation profile in a mammal can be used for identifying a mammal as having cancer. For example, cfDNA fragments obtained from a mammal (e.g., from a sample obtained from a mammal) can be subjected to low coverage whole-genome sequencing, and the sequenced fragments can be mapped to the genome (e.g., in non-overlapping windows) and assessed to determine a cfDNA fragmentation profile. This document also provides methods and materials for assessing and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile. In some cases, this document provides methods and materials for monitoring and/or treating a mammal having cancer. For example, one or more cancer treatments can be administered to a mammal identified as having cancer (e.g., based, at least in part, on a cfDNA fragmentation profile) to treat the mammal.

Described herein is a non-invasive method for the early detection and localization of cancer. cfDNA in the blood can provide a non-invasive diagnostic avenue for patients with cancer. As demonstrated herein, DNA Evaluation of Fragments for early Interception (DELFI) was developed and used to evaluate genome-wide fragmentation patterns of cfDNA of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers as well as 245 healthy individuals. These analyses revealed that cfDNA profiles of healthy individuals reflected nucleosomal fragmentation patterns of white blood cells, while patients with cancer had altered fragmentation profiles. DELFI had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity and identified the tissue of origin of the cancers to a limited number of sites in 75% of cases. Assessing cfDNA (e.g., using DELFI) can provide a screening approach for early detection of cancer, which can increase the chance for successful treatment of a patient having cancer. Assessing cfDNA (e.g., using DELFI) can also provide an approach for monitoring cancer, which can increase the chance for successful treatment and improved outcome of a patient having cancer. In addition, a cfDNA fragmentation profile can be obtained from limited amounts of cfDNA and using inexpensive reagents and/or instruments.

In general, one aspect of this document features methods for determining a cfDNA fragmentation profile of a mammal. The methods can include, or consist essentially of, processing cfDNA fragments obtained from a sample obtained from the mammal into sequencing libraries, subjecting the sequencing libraries to whole genome sequencing (e.g., low-coverage whole genome sequencing) to obtain sequenced fragments, mapping the sequenced fragments to a genome to obtain windows of mapped sequences, and analyzing the windows of mapped sequences to determine cfDNA fragment lengths. The mapped sequences can include tens to thousands of windows. The windows of mapped sequences can be non-overlapping windows. The windows of mapped sequences can each include about 5 million base pairs. The cfDNA fragmentation profile can be determined within each window. The cfDNA fragmentation profile can include a median fragment size. The cfDNA fragmentation profile can include a fragment size distribution. The cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments in the windows of mapped sequences. The cfDNA fragmentation profile can be over the whole genome. The cfDNA fragmentation profile can be over a subgenomic interval (e.g., an interval in a portion of a chromosome).

In another aspect, this document features methods for identifying a mammal as having cancer. The methods can include, or consist essentially of, determining a cfDNA fragmentation profile in a sample obtained from a mammal, comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile, and identifying the mammal as having cancer when the cfDNA fragmentation profile in the sample obtained from the mammal is different from the reference cfDNA fragmentation profile. The reference cfDNA fragmentation profile can be a cfDNA fragmentation profile of a healthy mammal. The reference cfDNA fragmentation profile can be generated by determining a cfDNA fragmentation profile in a sample obtained from the healthy mammal. The reference DNA fragmentation pattern can be a reference nucleosome cfDNA fragmentation profile. The cfDNA fragmentation profiles can include a median fragment size, and a median fragment size of the cfDNA fragmentation profile can be shorter than a median fragment size of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include a fragment size distribution, and a fragment size distribution of the cfDNA fragmentation profile can differ by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include position dependent differences in fragmentation patterns, including a ratio of small cfDNA fragments to large cfDNA fragments, where a small cfDNA fragment can be 100 base pairs (bp) to 150 bp in length and a large cfDNA fragments can be 151 bp to 220 bp in length, and where a correlation of fragment ratios in the cfDNA fragmentation profile can be lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include sequence coverage of small cfDNA fragments, large cfDNA fragments, or of both small and large cfDNA fragments, across the genome. The cancer can be colorectal cancer, lung cancer, breast cancer, bile duct cancer, pancreatic cancer, gastric cancer, or ovarian cancer. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile in windows across the whole genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over a subgenomic interval (e.g., an interval in a portion of a chromosome). The mammal can have been previously administered a cancer treatment to treat the cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof. The method also can include administering to the mammal a cancer treatment (e.g., surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof). The mammal can be monitored for the presence of cancer after administration of the cancer treatment.

In another aspect, this document features methods for treating a mammal having cancer. The methods can include, or consist essentially of, identifying the mammal as having cancer, where the identifying includes determining a cfDNA fragmentation profile in a sample obtained from the mammal, comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile, and identifying the mammal as having cancer when the cfDNA fragmentation profile obtained from the mammal is different from the reference cfDNA fragmentation profile; and administering a cancer treatment to the mammal. The mammal can be a human. The cancer can be colorectal cancer, lung cancer, breast cancer, gastric cancers, pancreatic cancers, bile duct cancers, or ovarian cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or combinations thereof. The reference cfDNA fragmentation profile can be a cfDNA fragmentation profile of a healthy mammal. The reference cfDNA fragmentation profile can be generated by determining a cfDNA fragmentation profile in a sample obtained from a healthy mammal. The reference DNA fragmentation pattern can be a reference nucleosome cfDNA fragmentation profile. The cfDNA fragmentation profile can include a median fragment size, where a median fragment size of the cfDNA fragmentation profile is shorter than a median fragment size of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include a fragment size distribution, where a fragment size distribution of the cfDNA fragmentation profile differs by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments in the windows of mapped sequences, where a small cfDNA fragment is 100 bp to 150 bp in length, where a large cfDNA fragments is 151 bp to 220 bp in length, and where a correlation of fragment ratios in the cfDNA fragmentation profile is lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include the sequence coverage of small cfDNA fragments in windows across the genome. The cfDNA fragmentation profile can include the sequence coverage of large cfDNA fragments in windows across the genome. The cfDNA fragmentation profile can include the sequence coverage of small and large cfDNA fragments in windows across the genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over the whole genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over a subgenomic interval. The mammal can have previously been administered a cancer treatment to treat the cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or combinations thereof. The method also can include monitoring the mammal for the presence of cancer after administration of the cancer treatment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used to practice the invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.

DESCRIPTION OF THE DRAWINGS

FIG. 1. Schematic of an exemplary DELFI approach. Blood is collected from a cohort of healthy individuals and patients with cancer. Nucleosome protected cfDNA is extracted from the plasma fraction, processed into sequencing libraries, examined through whole genome sequencing, mapped to the genome, and analyzed to determine cfDNA fragment profiles in different windows across the genome. Machine learning approaches are used to categorize individuals as healthy or as having cancer and to identify the tumor tissue of origin using genome-wide cfDNA fragmentation patterns.

FIG. 2. Simulations of non-invasive cancer detection based on number of alterations analyzed and tumor-derived cfDNA fragment distributions. Monte Carlo simulations were performed using different numbers of tumor-specific alterations to evaluate the probability of detecting cancer alterations in cfDNA at the indicated fraction of tumor-derived molecules. The simulations were performed assuming an average of 2000 genome equivalents of cfDNA and the requirement of five or more observations of any alteration. These analyses indicate that increasing the number of tumor-specific alterations improves the sensitivity of detection of circulating tumor DNA.

FIG. 3. Tumor-derived cfDNA fragment distributions. Cumulative density functions of cfDNA fragment lengths of 42 loci containing tumor-specific alterations from 30 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands (blue). Lengths of mutant cfDNA fragments were significantly different in size compared to wild-type cfDNA fragments (red) at these loci.

FIGS. 4A and 4B. Tumor-derived cfDNA GC content and fragment length. A, GC content was similar for mutated and non-mutated fragments. B, GC content was not correlated to fragment length.

FIG. 5. Germline cfDNA fragment distributions. Cumulative density functions of fragment lengths of 44 loci containing germline alterations (non-tumor derived) from 38 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands. Fragments with germline mutations (blue) were comparable in length to wild-type cfDNA fragment lengths (red).

FIG. 6. Hematopoietic cfDNA fragment distributions. Cumulative density functions of fragment lengths of 41 loci containing hematopoietic alterations (non-tumor derived) from 28 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands. After correction for multiple testing, there were no significant differences (α=0.05) in the size distributions of mutated hematopoietic cfDNA fragments (blue) and wild-type cfDNA fragments (red).

FIGS. 7A-7F. cfDNA fragmentation profiles in healthy individuals and patients with cancer. A, Genome-wide cfDNA fragmentation profiles (defined as the ratio of short to long fragments) from ˜9× whole genome sequencing are shown in 5 Mb bins for 30 healthy individuals (top) and 8 lung cancer patients (bottom). B, An analysis of healthy cfDNA (top), lung cancer cfDNA (middle), and healthy lymphocyte (bottom) fragmentation profiles and lymphocyte profiles from chromosome 1 at 1 Mb resolution. The healthy lymphocyte profiles were scaled with a standard deviation equal to that of the median healthy cfDNA profiles. Healthy cfDNA patterns closely mirrored those in healthy lymphocytes while lung cancer cfDNA profiles were more varied and differed from both healthy and lymphocyte profiles. C, Smoothed median distances between adjacent nucleosome centered at zero using 100 kb bins from healthy cfDNA (top) and nuclease-digested healthy lymphocytes (middle) are depicted together with the first eigenvector for the genome contact matrix obtained through previously reported Hi-C analyses of lymphoblastoid cells (bottom). Healthy cfDNA nucleosome distances closely mirrored those in nuclease-digested lymphocytes as well as those from lymphoblastoid Hi-C analyses. cfDNA fragmentation profiles from healthy individuals (n=30) had high correlations while patients with lung cancer had lower correlations to median fragmentation profiles of lymphocytes (D), healthy cfDNA (E), and lymphocyte nucleosome (F) distances.

FIG. 8. Density of cfDNA fragment lengths in healthy individuals and patients with lung cancer. cfDNA fragments lengths are shown for healthy individuals (n=30, gray) and patients with lung cancer (n=8, blue).

FIGS. 9A and 9B. Subsampling of whole genome sequence data for analysis of cfDNA fragmentation profiles. A, High coverage (9×) whole-genome sequencing data were subsampled to 2×, 1×, 0.5×, 0.2×, and 0.1× fold coverage. Mean centered genome-wide fragmentation profiles in 5 Mb bins for 30 healthy individuals and 8 patients with lung cancer are depicted for each subsampled fold coverage with median profiles shown in blue. B, Pearson correlation of subsampled profiles to initial profile at 9× coverage for healthy individuals and patients with lung cancer.

FIG. 10. cfDNA fragmentation profiles and sequence alterations during therapy. Detection and monitoring of cancer in serial blood draws from NSCLC patients (n=19) undergoing treatment with targeted tyrosine kinase inhibitors (black arrows) was performed using targeted sequencing (top) and genome-wide fragmentation profiles (bottom). For each case, the vertical axis of the lower panel displays −1 times the correlation of each sample to the median healthy cfDNA fragmentation profile. Error bars depict confidence intervals from binomial tests for mutant allele fractions and confidence intervals calculated using Fisher transformation for genome-wide fragmentation profiles. Although the approaches analyze different aspects of cfDNA (whole genome compared to specific alterations) the targeted sequencing and fragmentation profiles were similar for patients responding to therapy as well as those with stable or progressive disease. As fragmentation profiles reflect both genomic and epigenomic alterations, while mutant allele fractions only reflect individual mutations, mutant allele fractions alone may not reflect the absolute level of correlation of fragmentation profiles to healthy individuals.

FIGS. 11A-11C. cfDNA fragmentation profiles in healthy individuals and patients with cancer. A, Fragmentation profiles (bottom) in the context of tumor copy number changes (top) in a colorectal cancer patient where parallel analyses of tumor tissue were performed. The distribution of segment means and integer copy numbers are shown at top right in the indicated colors. Altered fragmentation profiles were present in regions of the genome that were copy neutral and were further affected in regions with copy number changes. B, GC adjusted fragmentation profiles from 1-2× whole genome sequencing for healthy individuals and patients with cancer are depicted per cancer type using 5 Mb windows. The median healthy profile is indicated in black and the 98% confidence band is shown in gray. For patients with cancer, individual profiles are colored based on their correlation to the healthy median. C, Windows are indicated in orange if more than 10% of the cancer samples had a fragment ratio more than three standard deviations from the median healthy fragment ratio. These analyses highlight the multitude of position dependent alterations across the genome in cfDNA of individuals with cancer.

FIGS. 12A and 12B. Profiles of cfDNA fragment lengths in copy neutral regions in healthy individuals and one patient with colorectal cancer. A, The fragmentation profile in 211 copy neutral windows in chromosomes 1-6 for 25 randomly selected healthy individuals (gray). For a patient with colorectal cancer (CGCRC291) with an estimated mutant allele fraction of 20%, the cancer fragment length profile was diluted to an approximate 10% tumor contribution (blue). A and B, While the marginal densities of the fragment profiles for the healthy samples and cancer patient show substantial overlap (A, right), the fragmentation profiles are different as can be seen visualization of the fragmentation profiles (A, left) and by the separation of the colorectal cancer patient from the healthy samples in a principal component analysis (B).

FIGS. 13A and 13B. Genome-wide GC correction of cfDNA fragments. To estimate and control for the effects of GC content on sequencing coverage, coverage in non-overlapping 100 kb genomic windows was calculated across the autosomes. For each window, the average GC of the aligned fragments was calculated. A, Loess smoothing of raw coverage (top row) for two randomly selected healthy subjects (CGPLH189 and CGPLH380) and two cancer patients (CGPLLU161 and CGPLBR24) with undetectable aneuploidy (PA score <2.35). After subtracting the average coverage predicted by the loess model, the residuals were resealed to the median autosomal coverage (bottom row). As fragment length may also result in coverage biases, this GC correction procedure was performed separately for short (≤150 bp) and long (≥151 bp) fragments. While the 100 kb bins on chromosome 19 (blue points) consistently have less coverage than predicted by the loess model, we did not implement a chromosome-specific correction as such an approach would remove the effects of chromosomal copy number on coverage. B, Overall, a limited correlation was found between short or long fragment coverage and GC content after correction among healthy subjects and cancer patients with a PA score <3.

FIG. 14. Schematic of machine learning model. Gradient tree boosting machine learning was used to examine whether cfDNA can be categorized as having characteristics of a cancer patient or healthy individual. The machine learning model included fragmentation size and coverage characteristics in windows throughout the genome, as well as chromosomal arm and mitochondrial DNA copy numbers. A 10-fold cross validation approach was employed in which each sample is randomly assigned to a fold and 9 of the folds (90% of the data) are used for training and one fold (10% of the data) is used for testing. The prediction accuracy from a single cross validation is an average over the 10 possible combinations of test and training sets. As this prediction accuracy can reflect bias from the initial randomization of patients, the entire procedure was repeat, including the randomization of patients to folds, 10 times. For all cases, feature selection and model estimation were performed on training data and were validated on test data and the test data were never used for feature selection. Ultimately, a DELFI score was obtained that could be used to classify individuals as likely healthy or having cancer.

FIG. 15. Distribution of AUCs across the repeated 10-fold cross-validation. The 25^(th), 50^(th), and 75^(th) percentiles of the 100 AUCs for the cohort of 215 healthy individuals and 208 patients with cancer are indicated by dashed lines.

FIGS. 16A and 16B. Whole-genome analyses of chromosomal arm copy number changes and mitochondrial genome representation. A, Z scores for each autosome arm are depicted for healthy individuals (n=215) and patients with cancer (n=208). The vertical axis depicts normal copy at zero with positive and negative values indicating arm gains and losses, respectively. Z scores greater than 50 or less than −50 are thresholded at the indicated values. B, The fraction of reads mapping to the mitochondrial genome is depicted for healthy individuals and patients with cancer.

FIGS. 17A and 17B. Detection of cancer using DELFI. A, Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and 208 patients with cancer (DELFI, AUC=0.94), with ≥95% specificity shaded in blue. Machine learning analyses of chromosomal arm copy number (Chr copy number (ML)), and mitochondrial genome copy number (mtDNA), are shown in the indicated colors. B, Analyses of individual cancers types using the DELFI-combined approach had AUCs ranging from 0.86 to >0.99.

FIG. 18. DELFI detection of cancer by stage. Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and each stage of 208 patients with cancer with >95% specificity shaded in blue.

FIG. 19. DELFI tissue of origin prediction. Receiver operator characteristics for DELFI tissue prediction of bile duct, breast, colorectal, gastric, lung, ovarian, and pancreatic cancers are depicted. In order to increase sample sizes within cancer type classes, cases detected with a 90% specificity were included, and the lung cancer cohort was supplemented with the addition of baseline cfDNA data from 18 lung cancer patients with prior treatment (see, e.g., Shen et at, 2018 Nature, 563:579-583).

FIG. 20. Detection of cancer using DELFI and mutation-based cfDNA approaches. DELFI (green) and targeted sequencing for mutation identification (blue) were performed independently in a cohort of 126 patients with breast, bile duct, colorectal, gastric, lung, or ovarian cancers. The number of individuals detected by each approach and in combination are indicated for DELFI detection with a specificity of 98%, targeted sequencing specificity at >99%, and a combined specificity of 98%. ND indicates not detected.

DETAILED DESCRIPTION

This document provides methods and materials for determining a cfDNA fragmentation profile in a mammal (e.g., in a sample obtained from a mammal). As used herein, the terms “fragmentation profile,” “position dependent differences in fragmentation patterns,” and “differences in fragment size and coverage in a position dependent manner across the genome” are equivalent and can be used interchangeably. In some cases, determining a cfDNA fragmentation profile in a mammal can be used for identifying a mammal as having cancer. For example, cfDNA fragments obtained from a mammal (e.g., from a sample obtained from a mammal) can be subjected to low coverage whole-genome sequencing, and the sequenced fragments can be mapped to the genome (e.g., in non-overlapping windows) and assessed to determine a cfDNA fragmentation profile. As described herein, a cfDNA fragmentation profile of a mammal having cancer is more heterogeneous (e.g., in fragment lengths) than a cfDNA fragmentation profile of a healthy mammal (e.g., a mammal not having cancer). As such, this document also provides methods and materials for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the presence and, optionally, the tissue of origin of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for monitoring a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the presence of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying a mammal as having cancer, and administering one or more cancer treatments to the mammal to treat the mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal, and one or more cancer treatments can be administered to the mammal.

A cfDNA fragmentation profile can include one or more cfDNA fragmentation patterns. A cfDNA fragmentation pattern can include any appropriate cfDNA fragmentation pattern. Examples of cfDNA fragmentation patterns include, without limitation, median fragment size, fragment size distribution, ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In some cases, a cfDNA fragmentation pattern includes two or more (e.g., two, three, or four) of median fragment size, fragment size distribution, ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In some cases, cfDNA fragmentation profile can be a genome-wide cfDNA profile (e.g., a genome-wide cfDNA profile in windows across the genome). In some cases, cfDNA fragmentation profile can be a targeted region profile. A targeted region can be any appropriate portion of the genome (e.g., a chromosomal region). Examples of chromosomal regions for which a cfDNA fragmentation profile can be determined as described herein include, without limitation, a portion of a chromosome (e.g., a portion of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and/or 14q) and a chromosomal arm (e.g., a chromosomal arm of 8q, 13q, 11q, and/or 3p). In some cases, a cfDNA fragmentation profile can include two or more targeted region profiles.

In some cases, a cfDNA fragmentation profile can be used to identify changes (e.g., alterations) in cfDNA fragment lengths. An alteration can be a genome-wide alteration or an alteration in one or more targeted regions/loci. A target region can be any region containing one or more cancer-specific alterations. Examples of cancer-specific alterations, and their chromosomal locations, include, without limitation, those shown in Table 3 (Appendix C) and those shown in Table 6 (Appendix F). In some cases, a cfDNA fragmentation profile can be used to identify (e.g., simultaneously identify) from about 10 alterations to about 500 alterations (e.g., from about 25 to about 500, from about 50 to about 500, from about 100 to about 500, from about 200 to about 500, from about 300 to about 500, from about 10 to about 400, from about 10 to about 300, from about 10 to about 200, from about 10 to about 100, from about 10 to about 50, from about 20 to about 400, from about 30 to about 300, from about 40 to about 200, from about 50 to about 100, from about 20 to about 100, from about 25 to about 75, from about 50 to about 250, or from about 100 to about 200, alterations).

In some cases, a cfDNA fragmentation profile can be used to detect tumor-derived DNA. For example, a cfDNA fragmentation profile can be used to detect tumor-derived DNA by comparing a cfDNA fragmentation profile of a mammal having, or suspected of having, cancer to a reference cfDNA fragmentation profile (e.g., a cfDNA fragmentation profile of a healthy mammal and/or a nucleosomal DNA fragmentation profile of healthy cells from the mammal having, or suspected of having, cancer). In some cases, a reference cfDNA fragmentation profile is a previously generated profile from a healthy mammal. For example, methods provided herein can be used to determine a reference cfDNA fragmentation profile in a healthy mammal, and that reference cfDNA fragmentation profile can be stored (e.g., in a computer or other electronic storage medium) for future comparison to a test cfDNA fragmentation profile in mammal having, or suspected of having, cancer. In some cases, a reference cfDNA fragmentation profile (e.g., a stored cfDNA fragmentation profile) of a healthy mammal is determined over the whole genome. In some cases, a reference cfDNA fragmentation profile (e.g., a stored cfDNA fragmentation profile) of a healthy mammal is determined over a subgenomic interval.

In some cases, a cfDNA fragmentation profile can be used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer).

A cfDNA fragmentation profile can include a cfDNA fragment size pattern. cfDNA fragments can be any appropriate size. For example, cfDNA fragment can be from about 50 base pairs (bp) to about 400 bp in length. As described herein, a mammal having cancer can have a cfDNA fragment size pattern that contains a shorter median cfDNA fragment size than the median cfDNA fragment size in a healthy mammal. A healthy mammal (e.g., a mammal not having cancer) can have cfDNA fragment sizes having a median cfDNA fragment size from about 166.6 bp to about 167.2 bp (e.g., about 166.9 bp). In some cases, a mammal having cancer can have cfDNA fragment sizes that are, on average, about 1.28 bp to about 2.49 bp (e.g., about 1.88 bp) shorter than cfDNA fragment sizes in a healthy mammal. For example, a mammal having cancer can have cfDNA fragment sizes having a median cfDNA fragment size of about 164.11 bp to about 165.92 bp (e.g., about 165.02 bp).

A cfDNA fragmentation profile can include a cfDNA fragment size distribution. As described herein, a mammal having cancer can have a cfDNA size distribution that is more variable than a cfDNA fragment size distribution in a healthy mammal. In some case, a size distribution can be within a targeted region. A healthy mammal (e.g., a mammal not having cancer) can have a targeted region cfDNA fragment size distribution of about 1 or less than about 1. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is longer (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 or more bp longer, or any number of base pairs between these numbers) than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is shorter (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 or more bp shorter, or any number of base pairs between these numbers) than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is about 47 bp smaller to about 30 bp longer than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution of, on average, a 10, 11, 12, 13, 14, 15, 15, 17, 18, 19, 20 or more bp difference in lengths of cfDNA fragments. For example, a mammal having cancer can have a targeted region cfDNA fragment size distribution of, on average, about a 13 bp difference in lengths of cfDNA fragments. In some case, a size distribution can be a genome-wide size distribution. A healthy mammal (e.g., a mammal not having cancer) can have very similar distributions of short and long cfDNA fragments genome-wide. In some cases, a mammal having cancer can have, genome-wide, one or more alterations (e.g., increases and decreases) in cfDNA fragment sizes. The one or more alterations can be any appropriate chromosomal region of the genome. For example, an alteration can be in a portion of a chromosome. Examples of portions of chromosomes that can contain one or more alterations in cfDNA fragment sizes include, without limitation, portions of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and 14q. For example, an alteration can be across a chromosome arm (e.g., an entire chromosome arm).

A cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments and a correlation of fragment ratios to reference fragment ratios. As used herein, with respect to ratios of small cfDNA fragments to large cfDNA fragments, a small cfDNA fragment can be from about 100 bp in length to about 150 bp in length. As used herein, with respect to ratios of small cfDNA fragments to large cfDNA fragments, a large cfDNA fragment can be from about 151 bp in length to 220 bp in length. As described herein, a mammal having cancer can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) that is lower (e.g., 2-fold lower, 3-fold lower, 4-fold lower, 5-fold lower, 6-fold lower, 7-fold lower, 8-fold lower, 9-fold lower, 10-fold lower, or more) than in a healthy mammal. A healthy mammal (e.g., a mammal not having cancer) can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) of about 1 (e.g., about 0.96). In some cases, a mammal having cancer can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) that is, on average, about 0.19 to about 0.30 (e.g., about 0.25) lower than a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) in a healthy mammal.

A cfDNA fragmentation profile can include coverage of all fragments. Coverage of all fragments can include windows (e.g., non-overlapping windows) of coverage. In some cases, coverage of all fragments can include windows of small fragments (e.g., fragments from about 100 bp to about 150 bp in length). In some cases, coverage of all fragments can include windows of large fragments (e.g., fragments from about 151 bp to about 220 bp in length).

In some cases, a cfDNA fragmentation profile can be used to identify the tissue of origin of a cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, or an ovarian cancer). For example, a cfDNA fragmentation profile can be used to identify a localized cancer. When a cfDNA fragmentation profile includes a targeted region profile, one or more alterations described herein (e.g., in Table 3 (Appendix C) and/or in Table 6 (Appendix F)) can be used to identify the tissue of origin of a cancer. In some cases, one or more alterations in chromosomal regions can be used to identify the tissue of origin of a cancer.

A cfDNA fragmentation profile can be obtained using any appropriate method. In some cases, cfDNA from a mammal (e.g., a mammal having, or suspected of having, cancer) can be processed into sequencing libraries which can be subjected to whole genome sequencing (e.g., low-coverage whole genome sequencing), mapped to the genome, and analyzed to determine cfDNA fragment lengths. Mapped sequences can be analyzed in non-overlapping windows covering the genome. Windows can be any appropriate size. For example, windows can be from thousands to millions of bases in length. As one non-limiting example, a window can be about 5 megabases (Mb) long. Any appropriate number of windows can be mapped. For example, tens to thousands of windows can be mapped in the genome. For example, hundreds to thousands of windows can be mapped in the genome. A cfDNA fragmentation profile can be determined within each window. In some cases, a cfDNA fragmentation profile can be obtained as described in Example 1. In some cases, a cfDNA fragmentation profile can be obtained as shown in FIG. 1.

In some cases, methods and materials described herein also can include machine learning. For example, machine learning can be used for identifying an altered fragmentation profile (e.g., using coverage of cfDNA fragments, fragment size of cfDNA fragments, coverage of chromosomes, and mtDNA).

In some cases, methods and materials described herein can be the sole method used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer). For example, determining a cfDNA fragmentation profile can be the sole method used to identify a mammal as having cancer.

In some cases, methods and materials described herein can be used together with one or more additional methods used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer). Examples of methods used to identify a mammal as having cancer include, without limitation, identifying one or more cancer-specific sequence alterations, identifying one or more chromosomal alterations (e.g., aneuploidies and rearrangements), and identifying other cfDNA alterations. For example, determining a cfDNA fragmentation profile can be used together with identifying one or more cancer-specific mutations in a mammal's genome to identify a mammal as having cancer. For example, determining a cfDNA fragmentation profile can be used together with identifying one or more aneuploidies in a mammal's genome to identify a mammal as having cancer.

In some aspects, this document also provides methods and materials for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying the location (e.g., the anatomic site or tissue of origin) of a cancer in a mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the tissue of origin of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying a mammal as having cancer, and administering one or more cancer treatments to the mammal to treat the mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal, and administering one or more cancer treatments to the mammal. In some cases, this document provides methods and materials for treating a mammal having cancer. For example, one or more cancer treatments can be administered to a mammal identified as having cancer (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal) to treat the mammal. In some cases, during or after the course of a cancer treatment (e.g., any of the cancer treatments described herein), a mammal can undergo monitoring (or be selected for increased monitoring) and/or further diagnostic testing. In some cases, monitoring can include assessing mammals having, or suspected of having, cancer by, for example, assessing a sample (e.g., a blood sample) obtained from the mammal to determine the cfDNA fragmentation profile of the mammal as described herein, and changes in the cfDNA fragmentation profiles over time can be used to identify response to treatment and/or identify the mammal as having cancer (e.g., a residual cancer).

Any appropriate mammal can be assessed, monitored, and/or treated as described herein. A mammal can be a mammal having cancer. A mammal can be a mammal suspected of having cancer. Examples of mammals that can be assessed, monitored, and/or treated as described herein include, without limitation, humans, primates such as monkeys, dogs, cats, horses, cows, pigs, sheep, mice, and rats. For example, a human having, or suspected of having, cancer can be assessed to determine a cfDNA fragmentation profiled as described herein and, optionally, can be treated with one or more cancer treatments as described herein.

Any appropriate sample from a mammal can be assessed as described herein (e.g., assessed for a DNA fragmentation pattern). In some cases, a sample can include DNA (e.g., genomic DNA). In some cases, a sample can include cfDNA (e.g., circulating tumor DNA (cfDNA)). In some cases, a sample can be fluid sample (e.g., a liquid biopsy). Examples of samples that can contain DNA and/or polypeptides include, without limitation, blood (e.g., whole blood, serum, or plasma), amnion, tissue, urine, cerebrospinal fluid, saliva, sputum, broncho-alveolar lavage, bile, lymphatic fluid, cyst fluid, stool, ascites, pap smears, breast milk, and exhaled breath condensate. For example, a plasma sample can be assessed to determine a cfDNA fragmentation profiled as described herein.

A sample from a mammal to be assessed as described herein (e.g., assessed for a DNA fragmentation pattern) can include any appropriate amount of cfDNA. In some cases, a sample can include a limited amount of DNA. For example, a cfDNA fragmentation profile can be obtained from a sample that includes less DNA than is typically required for other cfDNA analysis methods, such as those described in, for example, Phallen et al., 2017 Sci Transl Med 9; Cohen et al., 2018 Science 359:926; Newman et al., 2014 Nat Med 20:548; and Newman et al., 2016 Nat Biotechnol 34:547).

In some cases, a sample can be processed (e.g., to isolate and/or purify DNA and/or polypeptides from the sample). For example, DNA isolation and/or purification can include cell lysis (e.g., using detergents and/or surfactants), protein removal (e.g., using a protease), and/or RNA removal (e.g., using an RNase). As another example, polypeptide isolation and/or purification can include cell lysis (e.g., using detergents and/or surfactants), DNA removal (e.g., using a DNase), and/or RNA removal (e.g., using an RNase).

A mammal having, or suspected of having, any appropriate type of cancer can be assessed (e.g., to determine a cfDNA fragmentation profile) and/or treated (e.g., by administering one or more cancer treatments to the mammal) using the methods and materials described herein. A cancer can be any stage cancer. In some cases, a cancer can be an early stage cancer. In some cases, a cancer can be an asymptomatic cancer. In some cases, a cancer can be a residual disease and/or a recurrence (e.g., after surgical resection and/or after cancer therapy). A cancer can be any type of cancer. Examples of types of cancers that can be assessed, monitored, and/or treated as described herein include, without limitation, colorectal cancers, lung cancers, breast cancers, gastric cancers, pancreatic cancers, bile duct cancers, and ovarian cancers.

When treating a mammal having, or suspected of having, cancer as described herein, the mammal can be administered one or more cancer treatments. A cancer treatment can be any appropriate cancer treatment. One or more cancer treatments described herein can be administered to a mammal at any appropriate frequency (e.g., once or multiple times over a period of time ranging from days to weeks). Examples of cancer treatments include, without limitation adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors), targeted therapy such as administration of kinase inhibitors (e.g., kinase inhibitors that target a particular genetic lesion, such as a translocation or mutation), (e.g. a kinase inhibitor, an antibody, a bispecific antibody), signal transduction inhibitors, bispecific antibodies or antibody fragments (e.g., BiTEs), monoclonal antibodies, immune checkpoint inhibitors, surgery (e.g., surgical resection), or any combination of the above. In some cases, a cancer treatment can reduce the severity of the cancer, reduce a symptom of the cancer, and/or to reduce the number of cancer cells present within the mammal.

In some cases, a cancer treatment can include an immune checkpoint inhibitor. Non-limiting examples of immune checkpoint inhibitors include nivolumab (Opdivo), pembrolizumab (Keytruda), atezolizumab (tecentriq), avelumab (bavencio), durvalumab (imfinzi), ipilimumab (yervoy). See, e.g., Pardoll (2012) Nat. Rev Cancer 12: 252-264; Sun et al. (2017) Eur Rev Med Pharmacol Sci 21(6): 1198-1205; Hamanishi et al. (2015) J. Clin. Oncol. 33(34): 4015-22; Brahmer et al. (2012) N Engl J Med 366(26): 2455-65; Ricciuti et al. (2017) J. Thorac Oncol. 12(5): e51-e55; Ellis et al. (2017) Clin Lung Cancer pii: S1525-7304(17)30043-8; Zou and Awad (2017) Ann Oncol 28(4): 685-687; Sorscher (2017) N Engl J Med 376(10: 996-7; Hui et al. (2017) Ann Oncol 28(4): 874-881; Vansteenkiste et al. (2017) Expert Opin Biol Ther 17(6): 781-789; Hellmann et al. (2017) Lancet Oncol. 18(1): 31-41, Chen (2017) J. Chin Med Assoc 80(1): 7-14.

In some cases, a cancer treatment can be an adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors). See, e.g., Rosenberg and Restifo (2015) Science 348(6230): 62-68; Chang and Chen (2017) Trends Mol Med 23(5): 430-450; Yee and Lizee (2016) Cancer J. 23(2): 144-148; Chen et al. (2016) Oncoimmunology 6(2): e1273302; US 2016/0194404; US 2014/0050788; US 2014/0271635; U.S. Pat. No. 9,233,125; incorporated by reference in their entirety herein.

In some cases, a cancer treatment can be a chemotherapeutic agent. Non-limiting examples of chemotherapeutic agents include: amsacrine, azacitidine, axathioprine, bevacizumab (or an antigen-binding fragment thereof), bleomycin, busulfan, carboplatin, capecitabine, chlorambucil, cisplatin, cyclophosphamide, cytarabine, dacarbazine, daunorubicin, docetaxel, doxifluridine, doxorubicin, epirubicin, erlotinib hydrochlorides, etoposide, fiudarabine, floxuridine, fludarabine, fluorouracil, gemcitabine, hydroxyurea, idarubicin, ifosfamide, irinotecan, lomustine, mechlorethamine, melphalan, mercaptopurine, methotrxate, mitomycin, mitoxantrone, oxaliplatin, paclitaxel, pemetrexed, procarbazine, all-trans retinoic acid, streptozocin, tafluposide, temozolomide, teniposide, tioguanine, topotecan, uramustine, valrubicin, vinblastine, vincristine, vindesine, vinorelbine, and combinations thereof. Additional examples of anti-cancer therapies are known in the art; see, e.g. the guidelines for therapy from the American Society of Clinical Oncology (ASCO), European Society for Medical Oncology (ESMO), or National Comprehensive Cancer Network (NCCN).

When monitoring a mammal having, or suspected of having, cancer as described herein (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal), the monitoring can be before, during, and/or after the course of a cancer treatment. Methods of monitoring provided herein can be used to determine the efficacy of one or more cancer treatments and/or to select a mammal for increased monitoring. In some cases, the monitoring can include identifying a cfDNA fragmentation profile as described herein. For example, a cfDNA fragmentation profile can be obtained before administering one or more cancer treatments to a mammal having, or suspected or having, cancer, one or more cancer treatments can be administered to the mammal, and one or more cfDNA fragmentation profiles can be obtained during the course of the cancer treatment. In some cases, a cfDNA fragmentation profile can change during the course of cancer treatment (e.g., any of the cancer treatments described herein). For example, a cfDNA fragmentation profile indicative that the mammal has cancer can change to a cfDNA fragmentation profile indicative that the mammal does not have cancer. Such a cfDNA fragmentation profile change can indicate that the cancer treatment is working. Conversely, a cfDNA fragmentation profile can remain static (e.g., the same or approximately the same) during the course of cancer treatment (e.g., any of the cancer treatments described herein). Such a static cfDNA fragmentation profile can indicate that the cancer treatment is not working. In some cases, the monitoring can include conventional techniques capable of monitoring one or more cancer treatments (e.g., the efficacy of one or more cancer treatments). In some cases, a mammal selected for increased monitoring can be administered a diagnostic test (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to a mammal that has not been selected for increased monitoring. For example, a mammal selected for increased monitoring can be administered a diagnostic test at a frequency of twice daily, daily, bi-weekly, weekly, bi-monthly, monthly, quarterly, semi-annually, annually, or any at frequency therein. In some cases, a mammal selected for increased monitoring can be administered a one or more additional diagnostic tests compared to a mammal that has not been selected for increased monitoring. For example, a mammal selected for increased monitoring can be administered two diagnostic tests, whereas a mammal that has not been selected for increased monitoring is administered only a single diagnostic test (or no diagnostic tests). In some cases, a mammal that has been selected for increased monitoring can also be selected for further diagnostic testing. Once the presence of a tumor or a cancer (e.g., a cancer cell) has been identified (e.g., by any of the variety of methods disclosed herein), it may be beneficial for the mammal to undergo both increased monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and/or to assess the development of one or more cancer biomarkers such as mutations), and further diagnostic testing (e.g., to determine the size and/or exact location (e.g., tissue of origin) of the tumor or the cancer). In some cases, one or more cancer treatments can be administered to the mammal that is selected for increased monitoring after a cancer biomarker is detected and/or after the cfDNA fragmentation profile of the mammal has not improved or deteriorated. Any of the cancer treatments disclosed herein or known in the art can be administered. For example, a mammal that has been selected for increased monitoring can be further monitored, and a cancer treatment can be administered if the presence of the cancer cell is maintained throughout the increased monitoring period. Additionally or alternatively, a mammal that has been selected for increased monitoring can be administered a cancer treatment, and further monitored as the cancer treatment progresses. In some cases, after a mammal that has been selected for increased monitoring has been administered a cancer treatment, the increased monitoring will reveal one or more cancer biomarkers (e.g., mutations). In some cases, such one or more cancer biomarkers will provide cause to administer a different cancer treatment (e.g., a resistance mutation may arise in a cancer cell during the cancer treatment, which cancer cell harboring the resistance mutation is resistant to the original cancer treatment).

When a mammal is identified as having cancer as described herein (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal), the identifying can be before and/or during the course of a cancer treatment. Methods of identifying a mammal as having cancer provided herein can be used as a first diagnosis to identify the mammal (e.g., as having cancer before any course of treatment) and/or to select the mammal for further diagnostic testing. In some cases, once a mammal has been determined to have cancer, the mammal may be administered further tests and/or selected for further diagnostic testing. In some cases, methods provided herein can be used to select a mammal for further diagnostic testing at a time period prior to the time period when conventional techniques are capable of diagnosing the mammal with an early-stage cancer. For example, methods provided herein for selecting a mammal for further diagnostic testing can be used when a mammal has not been diagnosed with cancer by conventional methods and/or when a mammal is not known to harbor a cancer. In some cases, a mammal selected for further diagnostic testing can be administered a diagnostic test (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to a mammal that has not been selected for further diagnostic testing. For example, a mammal selected for further diagnostic testing can be administered a diagnostic test at a frequency of twice daily, daily, bi-weekly, weekly, bi-monthly, monthly, quarterly, semi-annually, annually, or any at frequency therein. In some cases, a mammal selected for further diagnostic testing can be administered a one or more additional diagnostic tests compared to a mammal that has not been selected for further diagnostic testing. For example, a mammal selected for further diagnostic testing can be administered two diagnostic tests, whereas a mammal that has not been selected for further diagnostic testing is administered only a single diagnostic test (or no diagnostic tests). In some cases, the diagnostic testing method can determine the presence of the same type of cancer (e.g., having the same tissue or origin) as the cancer that was originally detected (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal). Additionally or alternatively, the diagnostic testing method can determine the presence of a different type of cancer as the cancer that was original detected. In some cases, the diagnostic testing method is a scan. In some cases, the scan is a computed tomography (CT), a CT angiography (CTA), a esophagram (a Barium swallom), a Barium enema, a magnetic resonance imaging (MRI), a PET scan, an ultrasound (e.g., an endobronchial ultrasound, an endoscopic ultrasound), an X-ray, a DEXA scan. In some cases, the diagnostic testing method is a physical examination, such as an anoscopy, a bronchoscopy (e.g., an autofluorescence bronchoscopy, a white-light bronchoscopy, a navigational bronchoscopy), a colonoscopy, a digital breast tomosynthesis, an endoscopic retrograde cholangiopancreatography (ERCP), an ensophagogastroduodenoscopy, a mammography, a Pap smear, a pelvic exam, a positron emission tomography and computed tomography (PET-CT) scan. In some cases, a mammal that has been selected for further diagnostic testing can also be selected for increased monitoring. Once the presence of a tumor or a cancer (e.g., a cancer cell) has been identified (e.g., by any of the variety of methods disclosed herein), it may be beneficial for the mammal to undergo both increased monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and/or to assess the development of one or more cancer biomarkers such as mutations), and further diagnostic testing (e.g., to determine the size and/or exact location of the tumor or the cancer). In some cases, a cancer treatment is administered to the mammal that is selected for further diagnostic testing after a cancer biomarker is detected and/or after the cfDNA fragmentation profile of the mammal has not improved or deteriorated. Any of the cancer treatments disclosed herein or known in the art can be administered. For example, a mammal that has been selected for further diagnostic testing can be administered a further diagnostic test, and a cancer treatment can be administered if the presence of the tumor or the cancer is confirmed. Additionally or alternatively, a mammal that has been selected for further diagnostic testing can be administered a cancer treatment, and can be further monitored as the cancer treatment progresses. In some cases, after a mammal that has been selected for further diagnostic testing has been administered a cancer treatment, the additional testing will reveal one or more cancer biomarkers (e.g., mutations). In some cases, such one or more cancer biomarkers (e.g., mutations) will provide cause to administer a different cancer treatment (e.g., a resistance mutation may arise in a cancer cell during the cancer treatment, which cancer cell harboring the resistance mutation is resistant to the original cancer treatment).

The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES Example 1: Cell-Free DNA Fragmentation in Patients with Cancer

Analyses of cell free DNA have largely focused on targeted sequencing of specific genes. Such studies permit detection of a small number of tumor-specific alterations in patients with cancer and not all patients, especially those with early stage disease, have detectable changes. Whole genome sequencing of cell-free DNA can identify chromosomal abnormalities and rearrangements in cancer patients but detection of such alterations has been challenging in part due to the difficulty in distinguishing a small number of abnormal from normal chromosomal changes (Leary et al., 2010 Sci Transl Med 2:20ra14; and Leary et al., 2012 Sci Transl Med 4:162ra154). Other efforts have suggested nucleosome patterns and chromatin structure may be different between cancer and normal tissues, and that cfDNA in patients with cancer may result in abnormal cfDNA fragment size as well as position (Snyder et al., 2016 Cell 164:57; Jahr et al., 2001 Cancer Res 61:1659; Ivanov et al., 2015 BMC Genomics 16(Suppl 13):S1). However, the amount of sequencing needed for nucleosome footprint analyses of cfDNA is impractical for routine analyses.

The sensitivity of any cell-free DNA approach depends on the number of potential alterations examined as well as the technical and biological limitations of detecting such changes. As a typical blood sample contains ˜2000 genome equivalents of cfDNA per milliliter of plasma (Phallen et al., 2017 Sci Transl Med 9), the theoretical limit of detection of a single alteration can be no better than one in a few thousand mutant to wild-type molecules. An approach that detects a larger number of alterations in the same number of genome equivalents would be more sensitive for detecting cancer in the circulation. Monte Carlo simulations show that increasing the number of potential abnormalities detected from only a few to tens or hundreds can potentially improve the limit of detection by orders of magnitude, similar to recent probability analyses of multiple methylation changes in cfDNA (FIG. 2).

This study presents a novel method called DELFI for detection of cancer and further identification of tissue of origin using whole genome sequencing (FIG. 1). The approach uses cfDNA fragmentation profiles and machine learning to distinguish patterns of healthy blood cell DNA from tumor-derived DNA and to identify the primary tumor tissue. DELFI was used for a retrospective analysis of cfDNA from 245 healthy individuals and 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers, with most patients exhibiting localized disease. Assuming this approach had sensitivity ≥0.80 for discriminating cancer patients from healthy individuals while maintaining a specificity of 0.95, a study of at least 200 cancer patients would enable estimation of the true sensitivity with a margin of error of 0.06 at the desired specificity of 0.95 or greater.

Materials and Methods

Patient and Sample Characteristics

Plasma samples from healthy individuals and plasma and tissue samples from patients with breast, lung, ovarian, colorectal, bile duct, or gastric cancer were obtained from ILSBio/Bioreclamation, Aarhus University, Herlev Hospital of the University of Copenhagen, Hvidovre Hospital, the University Medical Center of the University of Utrecht, the Academic Medical Center of the University of Amsterdam, the Netherlands Cancer Institute, and the University of California, San Diego. All samples were obtained under Institutional Review Board approved protocols with informed consent for research use at participating institutions. Plasma samples from healthy individuals were obtained at the time of routine screening, including for colonoscopies or Pap smears. Individuals were considered healthy if they had no previous history of cancer and negative screening results.

Plasma samples from individuals with breast, colorectal, gastric, lung, ovarian, pancreatic, and bile duct cancer were obtained at the time of diagnosis, prior to tumor resection or therapy. Nineteen lung cancer patients analyzed for change in cfDNA fragmentation profiles across multiple time points were undergoing treatment with anti-EGFR or anti-ERBB2 therapy (see, e.g., Phallen et al., 2019 Cancer Research 15, 1204-1213). Clinical data for all patients included in this study are listed in Table 1 (Appendix A). Gender was confirmed through genomic analyses of X and Y chromosome representation. Pathologic staging of gastric cancer patients was performed after neoadjuvant therapy. Samples where the tumor stage was unknown were indicated as stage X or unknown.

Nucleosomal DNA Purification

Viably frozen lymphocytes were elutriated from leukocytes obtained from a healthy male (C0618) and female (D0808-L) (Advanced Biotechnologies Inc., Eldersburg, Md.). Aliquots of 1×10⁶ cells were used for nucleosomal DNA purification using EZ Nucleosomal DNA Prep Kit (Zymo Research, Irvine, Calif.). Cells were initially treated with 100 μl of Nuclei Prep Buffer and incubated on ice for 5 minutes. After centrifugation at 200 g for 5 minutes, supernatant was discarded and pelleted nuclei were treated twice with 100 μl of Atlantis Digestion Buffer or with 100 μl of micrococcal nuclease (MN) Digestion Buffer. Finally, cellular nucleic DNA was fragmented with 0.5 U of Atlantis dsDNase at 42° C. for 20 minutes or 1.5 U of MNase at 37° C. for 20 minutes. Reactions were stopped using 5×MN Stop Buffer and DNA was purified using Zymo-Spin™ IIC Columns. Concentration and quality of eluted cellular nucleic DNA were analyzed using the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.).

Sample Preparation and Sequencing of cfDNA

Whole blood was collected in EDTA tubes and processed immediately or within one day after storage at 4° C., or was collected in Streck tubes and processed within two days of collection for three cancer patients who were part of the monitoring analysis. Plasma and cellular components were separated by centrifugation at 800 g for 10 min at 4° C. Plasma was centrifuged a second time at 18,000 g at room temperature to remove any remaining cellular debris and stored at −80° C. until the time of DNA extraction. DNA was isolated from plasma using the Qiagen Circulating Nucleic Acids Kit (Qiagen GmbH) and eluted in LoBind tubes (Eppendorf AG). Concentration and quality of cfDNA were assessed using the Bioanalyzer 2100 (Agilent Technologies).

NGS cfDNA libraries were prepared for whole genome sequencing and targeted sequencing using 5 to 250 ng of cfDNA as described elsewhere (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415). Briefly, genomic libraries were prepared using the NEBNext DNA Library Prep Kit for Illumina [New England Biolabs (NEB)] with four main modifications to the manufacturer's guidelines: (i) The library purification steps used the on-bead AMPure XP approach to minimize sample loss during elution and tube transfer steps (see, e.g., Fisher et al., 2011 Genome Biol 12:R1); (ii) NEBNext End Repair, A-tailing, and adapter ligation enzyme and buffer volumes were adjusted as appropriate to accommodate the on-bead AMPure XP purification strategy; (iii) a pool of eight unique Illumina dual index adapters with 8-base pair (bp) barcodes was used in the ligation reaction instead of the standard Illumina single or dual index adapters with 6- or 8-bp barcodes, respectively; and (iv) cfDNA libraries were amplified with Phusion Hot Start Polymerase.

Whole genome libraries were sequenced directly. For targeted libraries, capture was performed using Agilent SureSelect reagents and a custom set of hybridization probes targeting 58 genes (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415) per the manufacturer's guidelines. The captured library was amplified with Phusion Hot Start Polymerase (NEB). Concentration and quality of captured cfDNA libraries were assessed on the Bioanalyzer 2100 using the DNA1000 Kit (Agilent Technologies). Targeted libraries were sequenced using 100-bp paired-end runs on the Illumina HiSeq 2000/2500 (Illumina).

Analyses of Targeted Sequencing Data from cfDNA

Analyses of targeted NGS data for cfDNA samples was performed as described elsewhere (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415). Briefly, primary processing was completed using Illumina CASAVA (Consensus Assessment of Sequence and Variation) software (version 1.8), including demultiplexing and masking of dual-index adapter sequences. Sequence reads were aligned against the human reference genome (version hg18 or hg19) using NovoAlign with additional realignment of select regions using the Needleman-Wunsch method (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53). The positions of the sequence alterations have not been affected by the different genome builds. Candidate mutations, consisting of point mutations, small insertions, and deletions, were identified using VariantDx (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53) (Personal Genome Diagnostics, Baltimore, Md.) across the targeted regions of interest.

To analyze the fragment lengths of cfDNA molecules, each read pair from a cfDNA molecule was required to have a Phred quality score ≥30. All duplicate cfDNA fragments, defined as having the same start, end, and index barcode were removed. For each mutation, only fragments for which one or both of the read pairs contained the mutated (or wild-type) base at the given position were included. This analysis was done using the R packages Rsamtools and GenomicAlignments.

For each genomic locus where a somatic mutation was identified, the lengths of fragments containing the mutant allele were compared to the lengths of fragments of the wild-type allele. If more than 100 mutant fragments were identified, Welch's two-sample t-test was used to compare the mean fragment lengths. For loci with fewer than 100 mutant fragments, a bootstrap procedure was implemented. Specifically, replacement N fragments containing the wild-type allele, where N denotes the number of fragments with the mutation, were sampled. For each bootstrap replicate of wild type fragments their median length was computed. The p-value was estimated as the fraction of bootstrap replicates with a median wild-type fragment length as or more extreme than the observed median mutant fragment length.

Analyses of Whole Genome Sequencing Data from cfDNA

Primary processing of whole genome NGS data for cfDNA samples was performed using Illumina CASAVA (Consensus Assessment of Sequence and Variation) software (version 1.8.2), including demultiplexing and masking of dual-index adapter sequences. Sequence reads were aligned against the human reference genome (version hg19) using ELAND.

Read pairs with a MAPQ score below 30 for either read and PCR duplicates were removed. hg19 autosomes were tiled into 26,236 adjacent, non-overlapping 100 kb bins. Regions of low mappability, indicated by the 10% of bins with the lowest coverage, were removed (see, e.g., Fortin et al., 2015 Genome Biol 16:180), as were reads falling in the Duke blacklisted regions (see, e.g., hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeMapability/). Using this approach, 361 Mb (13%) of the hg19 reference genome was excluded, including centromeric and telomeric regions. Short fragments were defined as having a length between 100 and 150 bp and long fragments were defined has having a length between 151 and 220 bp.

To account for biases in coverage attributable to GC content of the genome, the locally weighted smoother loess with span ¾ was applied to the scatterplot of average fragment GC versus coverage calculated for each 100 kb bin. This loess regression was performed separately for short and long fragments to account for possible differences in GC effects on coverage in plasma by fragment length (see, e.g., Benjamini et al., 2012 Nucleic Acids Res 40:e72). The predictions for short and long coverage explained by GC from the loess model were subtracted, obtaining residuals for short and long that were uncorrelated with GC. The residuals were returned to the original scale by adding back the genome-wide median short and long estimates of coverage. This procedure was repeated for each sample to account for possible differences in GC effects on coverage between samples. To further reduce the feature space and noise, the total GC-adjusted coverage in 5 Mb bins was calculated.

To compare the variability of fragment lengths from healthy subjects to fragments in patients with cancer, the standard deviation of the short to long fragmentation profiles for each individual was calculated. The standard deviations in the two groups were compared by a Wilcoxon rank sum test.

Analyses of Chromosome Arm Copy Number Changes

To develop arm-level statistics for copy number changes, an approach for aneuploidy detection in plasma as described elsewhere (see, e.g., Leary et al., 2012 Sci Trans Med 4:162ra154) was adopted. This approach divides the genome into non-overlapping 50 KB bins for which GC-corrected log 2 read depth was obtained after correction by loess with span ¾. This loess-based correction is comparable to the approach outlined above, but is evaluated on a log 2 scale to increase robustness to outliers in the smaller bins and does not stratify by fragment length. To obtain an arm-specific Z-score for copy number changes, the mean GC-adjusted read depth for each arm (GR) was centered and scaled by the average and standard deviation, respectively, of GR scores obtained from an independent set of 50 healthy samples.

Analyses of Mitochondrial-Aligned Reads from cfDNA

Whole genome sequence reads that initially mapped to the mitochondrial genome were extracted from barn files and realigned to the hg19 reference genome in end-to-end mode with Bowtie2 as described elsewhere (see, e.g., Langmead et al., 2012 Nat Methods 9:357-359). The resulting aligned reads were filtered such that both mates aligned to the mitochondrial genome with MAPQ>=30. The number of fragments mapping to the mitochondrial genome was counted and converted to a percentage of the total number of fragments in the original barn files.

Prediction Model for Cancer Classification

To distinguish healthy from cancer patients using fragmentation profiles, a stochastic gradient boosting model was used (gbm; see, e.g., Friedman et al, 2001 Ann Stat 29:1189-1232; and Friedman et al., 2002 Comput Stat Data An 38:367-378). GC-corrected total and short fragment coverage for all 504 bins were centered and scaled for each sample to have mean 0 and unit standard deviation. Additional features included Z-scores for each of the 39 autosomal arms and mitochondrial representation (log 10-transformed proportion of reads mapped to the mitochondria). To estimate the prediction error of this approach, 10-fold cross-validation was used as described elsewhere (see, e.g., Efron et al., 1997 J Am Stat Assoc 92, 548-560). Feature selection, performed only on the training data in each cross-validation run, removed bins that were highly correlated (correlation >0.9) or had near zero variance. Stochastic gradient boosted machine learning was implemented using the R package gbm package with parameters n.trees=150, interaction.depth=3, shrinkage=0.1, and n.minobsinside=10. To average over the prediction error from the randomization of patients to folds, the 10-fold cross validation procedure was repeated 10 times. Confidence intervals for sensitivity fixed at 98% and 95% specificity were obtained from 2000 bootstrap replicates.

Prediction Model for Tumor Tissue of Origin Classification

For samples correctly classified as cancer patients at 90% specificity (n=174), a separate stochastic gradient boosting model was trained to classify the tissue of origin. To account for the small number of lung samples used for prediction, 18 cfDNA baseline samples from late stage lung cancer patients were included from the monitoring analyses. Performance characteristics of the model were evaluated by 10-fold cross-validation repeated 10 times. This gbm model was trained using the same features as in the cancer classification model. As previously described, features that displayed correlation above 0.9 to each other or had near zero variance were removed within each training dataset during cross-validation. The tissue class probabilities were averaged across the 10 replicates for each patient and the class with the highest probability was taken as the predicted tissue.

Analyses of Nucleosomal DNA from Human Lymphocytes and cfDNA

From the nuclease treated lymphocytes, fragment sizes were analyzed in 5 Mb bins as described for whole genome cfDNA analyses. A genome-wide map of nucleosome positions was constructed from the nuclease treated lymphocyte cell-lines. This approach identified local biases in the coverage of circulating fragments, indicating a region protected from degradation. A “Window positioning score” (WPS) was used to score each base pair in the genome (see, e.g., Snyder et al., 2016 Cell 164:57). Using a sliding window of 60 bp centered around each base, the WPS was calculated as the number of fragments completely spanning the window minus the number of fragments with only one end in the window. Since fragments arising from nucleosomes have a median length of 167 bp, a high WPS indicated a possible nucleosomic position. WPS scores were centered at zero using a running median and smoothed using a Kolmogorov-Zurbenko filter (see, e.g., Zurbenko, The spectral analysis of lime series. North-Holland series in statistics and probability; Elsevier, New York, N.Y., 1986). For spans of positive WPS between 50 and 450 bp, a nucleosome peak was defined as the set of base pairs with a WPS above the median in that window. The calculation of nucleosome positions for cfDNA from 30 healthy individuals with sequence coverage of 9× was determined in the same manner as for lymphocyte DNA. To ensure that nucleosomes in healthy cfDNA were representative, a consensus track of nucleosomes was defined consisting only of nucleosomes identified in two or more individuals. Median distances between adjacent nucleosomes were calculated from the consensus track.

Monte Carlo Simulation of Detection Sensitivity

A Monte Carlo simulation was used to estimate the probability of detecting a molecule with a tumor-derived alteration. Briefly, 1 million molecules were generated from a multinomial distribution. For a simulation with m alterations, wild-type molecules were simulated with probability p and each of the m tumor alterations were simulated with probability (1−p)/m. Next, g*m molecules were sampled randomly with replacement, where g denotes the number of genome equivalents in 1 ml of plasma. If a tumor alteration was sampled s or more times, the sample was classified as cancer-derived. The simulation was repeated 1000 times, estimating the probability that the in silico sample would be correctly classified as cancer by the mean of the cancer indicator. Setting g=2000 and s=5, the number of tumor alterations was varied by powers of 2 from 1 to 256 and the fraction of tumor-derived molecules from 0.0001% to 1%.

Statistical Analyses

All statistical analyses were performed using R version 3.4.3. The R packages caret (version 6.0-79) and gbm (version 2.1-4) were used to implement the classification of healthy versus cancer and tissue of origin. Confidence intervals from the model output were obtained with the pROC (version 1.13) R package (see, e.g., Robin et al., 2011 BMC bioinformatics 12:77). Assuming the prevalence of undiagnosed cancer cases in this population is high (1 or 2 cases per 100 healthy), a genomic assay with a specificity of 0.95 and sensitivity of 0.8 would have useful operating characteristics (positive predictive value of 0.25 and negative predictive value near 1). Power calculations suggest that an analysis of more than 200 cancer patients and an approximately equal number of healthy controls, enable an estimation of the sensitivity with a margin of error of 0.06 at the desired specificity of 0.95 or greater.

Data and Code Availability

Sequence data utilized in this study have been deposited at the European Genome-phenome Archive under study accession nos. EGAS00001003611 and EGAS00001002577. Code for analyses is available at github.com/Cancer-Genomics/delfi_scripts.

Results

DELFT allows simultaneous analysis of a large number of abnormalities in cfDNA through genome-wide analysis of fragmentation patterns. The method is based on low coverage whole genome sequencing and analysis of isolated cfDNA. Mapped sequences are analyzed in non-overlapping windows covering the genome. Conceptually, windows may range in size from thousands to millions of bases, resulting in hundreds to thousands of windows in the genome. 5 Mb windows were used for evaluating cfDNA fragmentation patterns as these would provide over 20,000 reads per window even at a limited amount of 1-2× genome coverage. Within each window, the coverage and size distribution of cfDNA fragments was examined. This approach was used to evaluate the variation of genome-wide fragmentation profiles in healthy and cancer populations (Table 1; Appendix A). The genome-wide pattern from an individual can be compared to reference populations to determine if the pattern is likely healthy or cancer-derived. As genome-wide profiles reveal positional differences associated with specific tissues that may be missed in overall fragment size distributions, these patterns may also indicate the tissue source of cfDNA.

The fragmentation size of cfDNA was focused on as it was found that cancer-derived cfDNA molecules may be more variable in size than cfDNA derived from non-cancer cells. cfDNA fragments from targeted regions that were captured and sequenced at high coverage (43,706 total coverage, 8,044 distinct coverage) from patients with breast, colorectal, lung or ovarian cancer (Table 1 (Appendix A), Table 2 (Appendix B), and Table 3 (Appendix C)) were initially examined. Analyses of loci containing 165 tumor-specific alterations from 81 patients (range of 1-7 alterations per patient) revealed an average absolute difference of 6.5 bp (95% CI, 5.4-7.6 bp) between lengths of median mutant and wild-type cfDNA fragments (FIG. 3, Table 3 (Appendix C)). The median size of mutant cfDNA fragments ranged from 30 bases smaller at chromosome 3 position 41,266,124 to 47 bases larger at chromosome 11 position 108,117,753 than the wild-type sequences at these regions (Table 3; Appendix C). GC content was similar for mutated and non-mutated fragments (FIG. 4a ), and there was no correlation between GC content and fragment length (FIG. 4b ). Similar analyses of 44 germline alterations from 38 patients identified median cfDNA size differences of less than 1 bp between fragment lengths of different alleles (FIG. 5, Table 3 (Appendix C)). Additionally, 41 alterations related to clonal hematopoiesis were identified through a previous sequence comparison of DNA from plasma, buffy coat, and tumors of the same individuals. Unlike tumor-derived fragments, there were no significant differences between fragments with hematopoietic alterations and wild type fragments (FIG. 6, Table 3 (Appendix C)). Overall, cancer-derived cfDNA fragment lengths were significantly more variable compared to non-cancer cfDNA fragments at certain genomic regions (p<0.001, variance ratio test). It was hypothesized that these differences may be due to changes in higher-order chromatin structure as well as other genomic and epigenomic abnormalities in cancer and that cfDNA fragmentation in a position-specific manner could therefore serve as a unique biomarker for cancer detection.

As targeted sequencing only analyzes a limited number of loci, larger-scale genome-wide analyses to detect additional abnormalities in cfDNA fragmentation were investigated. cfDNA was isolated from ˜4 ml of plasma from 8 lung cancer patients with stage I-III disease, as well as from 30 healthy individuals (Table 1 (Appendix A), Table 4 (Appendix D), and Table 5 (Appendix E)). A high efficiency approach was used to convert cfDNA to next generation sequencing libraries and performed whole genome sequencing at ˜9× coverage (Table 4; Appendix D). Overall cfDNA fragment lengths of healthy individuals were larger, with a median fragment size of 167.3 bp, while patients with cancer had median fragment sizes of 163.8 (p<0.01, Welch's t-test) (Table 5; Appendix E). To examine differences in fragment size and coverage in a position dependent manner across the genome, sequenced fragments were mapped to their genomic origin and fragment lengths were evaluated in 504 windows that were 5 Mb in size, covering ˜2.6 Gb of the genome. For each window, the fraction of small cfDNA fragments (100 to 150 bp in length) to larger cfDNA fragments (151 to 220 bp) as well as overall coverage were determined and used to obtain genome-wide fragmentation profiles for each sample.

Healthy individuals had very similar fragmentation profiles throughout the genome (FIG. 7 and FIG. 8). To examine the origins of fragmentation patterns normally observed in cfDNA, nuclei were isolated from elutriated lymphocytes of two healthy individuals and treated with DNA nucleases to obtain nucleosomal DNA fragments. Analyses of cfDNA patterns in observed healthy individuals revealed a high correlation to lymphocyte nucleosomal DNA fragmentation profiles (FIGS. 7b and 7d ) and nucleosome distances (FIGS. 7c and 7f ). Median distances between nucleosomes in lymphocytes were correlated to open (A) and closed (B) compartments of lymphoblastoid cells as revealed using the Hi-C method (see, e.g., Lieberman-Aiden et al., 2009 Science 326:289-293; and Fortin et al., 2015 Genome Biol 16:180) for examining the three-dimensional architecture of genomes (FIG. 7c ). These analyses suggest that the fragmentation patterns of normal cfDNA are the result of nucleosomal DNA patterns that largely reflect the chromatin structure of normal blood cells.

In contrast to healthy cfDNA, patients with cancer had multiple distinct genomic differences with increases and decreases in fragment sizes at different regions (FIGS. 7a and 7b ). Similar to our observations from targeted analyses, there was also greater variation in fragment lengths genome-wide for patients with cancer compared to healthy individuals.

To determine whether cfDNA fragment length patterns could be used to distinguish patients with cancer from healthy individuals, genome-wide correlation analyses were performed of the fraction of short to long cfDNA fragments for each sample compared to the median fragment length profile calculated from healthy individuals (FIGS. 7a, 7b, and 7e ). While the profiles of cfDNA fragments were remarkably consistent among healthy individuals (median correlation of 0.99), the median correlation of genome-wide fragment ratios among cancer patients was 0.84 (0.15 lower, 95% CI 0.07-0.50, p<0.001, Wilcoxon rank sum test; Table 5 (Appendix E)). Similar differences were observed when comparing fragmentation profiles of cancer patients to fragmentation profiles or nucleosome distances in healthy lymphocytes (FIGS. 7c, 7d, and 7f ). To account for potential biases in the fragmentation profiles attributable to GC content, a locally weighted smoother was applied independently to each sample and found that differences in fragmentation profiles between healthy individuals and cancer patients remained after this adjustment (median correlation of cancer patients to healthy=0.83) (Table 5; Appendix E).

Sub sampling analyses of whole genome sequence data was performed at 9× coverage from cfDNA of patients with cancer at ˜2×, ˜1×, ˜0.5×, ˜0.2×, and ˜0.1× genome coverage, and it was determined that altered fragmentation profiles were readily identified even at 0.5× genome coverage (FIG. 9). Based on these observations, whole genome sequencing was performed with coverage of 1-2× to evaluate whether fragmentation profiles may change during the course of targeted therapy in a manner similar to monitoring of sequence alterations. cfDNA from 19 non-small cell lung cancer patients including 5 with partial radiographic response, 8 with stable disease, 4 with progressive disease, and 2 with unmeasurable disease, during the course of anti-EGFR or anti-ERBB2 therapy was evaluated (Table 6; Appendix F). As shown in FIG. 10, the degree of abnormality in the fragmentation profiles during therapy closely matched levels of EGFR or ERBB2 mutant allele fractions as determined using targeted sequencing (Spearman correlation of mutant allele fractions to fragmentation profiles=0.74). This correlation is remarkable as genome-wide and mutation-based methods are orthogonal and examine different cfDNA alterations that may be suppressed in these patients due to prior therapy. Notably all cases that had progression free survival of six or more months displayed a drop of or had extremely low levels of cfDNA after initiation of therapy as determined by fragmentation profiles, while cases with poor clinical outcome had increases in cfDNA. These results demonstrate the feasibility of fragmentation analyses for detecting the presence of tumor-derived cfDNA, and suggests that such analyses may also be useful for quantitative monitoring of cancer patients during treatment.

The fragmentation profiles were examined in the context of known copy number changes in a patient where parallel analyses of tumor tissue were obtained. These analyses demonstrated that altered fragmentation profiles were present in regions of the genome that were copy neutral and that these may be further affected in regions with copy number changes (FIG. 11a and FIG. 12a ). Position dependent differences in fragmentation patterns could be used to distinguish cancer-derived cfDNA from healthy cfDNA in these regions (FIG. 12a, b ), while overall cfDNA fragment size measurements would have missed such differences (FIG. 12a ).

These analyses were extended to an independent cohort of cancer patients and healthy individuals. Whole genome sequencing of cfDNA at 1-2× coverage from a total of 208 patients with cancer, including breast (n=54), colorectal (n=27), lung (n=12), ovarian (n=28), pancreatic (n=34), gastric (n=27), or bile duct cancers (n=26), as well as 215 individuals without cancer was performed (Table 1 (Appendix A) and Table 4 (Appendix D)). All cancer patients were treatment na

ve and the majority had resectable disease (n=183). After GC adjustment of short and long cfDNA fragment coverage (FIG. 13a ), coverage and size characteristics of fragments in windows throughout the genome were examined (FIG. 11b , Table 4 (Appendix D) and Table 7 (Appendix G)). Genome-wide correlations of coverage to GC content were limited and no differences in these correlations between cancer patients and healthy individuals were observed (FIG. 13b ). Healthy individuals had highly concordant fragmentation profiles, while patients with cancer had high variability with decreased correlation to the median healthy profile (Table 7; Appendix G). An analysis of the most commonly altered fragmentation windows in the genome among cancer patients revealed a median of 60 affected windows across the cancer types analyzed, highlighting the multitude of position dependent alterations in fragmentation of cfDNA in individuals with cancer (FIG. 11c ).

To determine if position dependent fragmentation changes can be used to detect individuals with cancer, a gradient tree boosting machine learning model was implemented to examine whether cfDNA can be categorized as having characteristics of a cancer patient or healthy individual and estimated performance characteristics of this approach by ten-fold cross validation repeated ten times (FIGS. 14 and 15). The machine learning model included GC-adjusted short and long fragment coverage characteristics in windows throughout the genome. A machine learning classifier for copy number changes from chromosomal arm dependent features rather than a single score was also developed (FIG. 16a and Table 8 (Appendix H)) and mitochondrial copy number changes were also included (FIG. 16b ) as these could also help distinguish cancer from healthy individuals. Using this implementation of DELFI, a score was obtained that could be used to classify patients as healthy or having to cancer. 152 of the 208 cancer patients were detected (73% sensitivity, 95% CI 67%-79%) while four of the 215 healthy individuals were misclassified (98% specificity) (Table 9). At a threshold of 95% specificity, 80% of patients with cancer were detected (95% CI, 74%-85%), including 79% of resectable (stage I-III) patients (145 of 183) and 82% of metastatic (stage IV) patients (18 out of 22) (Table 9). Receiver operator characteristic analyses for detection of patients with cancer had an AUC of 0.94 (95% CI 0.92-0.96), ranged among cancer types from 0.86 for pancreatic cancer to ≥0.99 for lung and ovarian cancers (FIGS. 17a and 17b ), and had AUCs≥0.92 across all stages (FIG. 18). The DELFI classifier score did not differ with age among either cancer patients or healthy individuals (Table 1; Appendix A).

TABLE 9 DELFI performance for cancer detection. 95% specificity 98% specificity Individuals Individuals Individuals analyzed detected Sensitivity 95% CI detected Sensitivity 95% CI Healthy 215 10 — — 4 — — Cancer 208 166 80% 74%-85% 152 73% 67%-79% Type Breast 54 38 70% 56%-82% 31 57% 43%-71% Bile duct 26 23 88% 70%-98% 21 81% 61%-93% Colorectal 27 22 81% 62%-94% 19 70% 50%-86% Gastric 27 22 81% 62%-94% 22 81% 62%-94% Lung 12 12 100%   74%-100% 12 100%   74%-100% Ovarian 28 25 89% 72%-98% 25 89% 72%-98% Pancreatic 34 24 71% 53%-85% 22 65% 46%-80% Stage I 41 30 73% 53%-86% 28 68% 52%-82% II 109 85 78% 69%-85% 78 72% 62%-80% III 33 30 91% 76%-98% 26 79% 61%-91% IV 22 18 82% 60%-95% 17 77% 55%-92% O, X 3 3 100%   29%-100% 3 100%   29%-100%

To assess the contribution of fragment size and coverage, chromosome arm copy number, or mitochondrial mapping to the predictive accuracy of the model, the repeated 10-fold cross-validation procedure was implemented to assess performance characteristics of these features in isolation. It was observed that fragment coverage features alone (AUC=0.94) were nearly identical to the classifier that combined all features (AUC=0.94) (FIG. 17a ). In contrast, analyses of chromosomal copy number changes had lower performance (AUC=0.88) but were still more predictive than copy number changes based on individual scores (AUC=0.78) or mitochondrial mapping (AUC=0.72) (FIG. 17a ). These results suggest that fragment coverage is the major contributor to our classifier. Including all features in the prediction model may contribute in a complementary fashion for detection of patients with cancer as they can be obtained from the same genome sequence data.

As fragmentation profiles reveal regional differences in fragmentation that may differ between tissues, a similar machine learning approach was used to examine whether cfDNA patterns could identify the tissue of origin of these tumors. It was found that this approach had a 61% accuracy (95% CI 53%-67%), including 76% for breast, 44% for bile duct, 71% for colorectal, 67% for gastric, 53% for lung, 48% for ovarian, and 50% for pancreatic cancers (FIG. 19, Table 10). The accuracy increased to 75% (95% CI 69%-81%) when considering assigning patients with abnormal cfDNA to one of two sites of origin (Table 10). For all tumor types, the classification of the tissue of origin by DELFI was significantly higher than determined by random assignment (p<0.01, binomial test, Table 10).

TABLE 10 DELFI tissue of origin prediction Cancer Patients Top Prediction Top Two Predictions Random Assignment Type Detected* Patients Accuracy (95% CI) Patients Accuracy (95% CI) Patients Accuracy Breast 42 32 76% (61%-88%) 38 91% (77%-97%) 9 22% Bile Duct 23 10 44% (23%-66%) 15 65% (43%-84%) 3 12% Colorectal 24 17 71% (49%-87%) 19 79% (58%-93%) 3 12% Gastric 24 16 67% (45%-84%) 19 79% (58%-93%) 3 12% Lung 30 16 53% (34%-72%) 23 77% (58%-90%) 2  6% Ovarian 27 13 48% (29%-68%) 16 59% (38%-78%) 4 14% Pancreatic 24 12 50% (29%-71%) 16 67% (45%-84%) 3 12% Total 194 116 61% (53%-67%) 146 75% (69%-81%) 26 13% *Patients detected are based on DELFI detection at 90% specificity. Lung cohort includes additional lung cancer patients with prior therapy.

As cancer-specific sequence alterations can be used to identify patients with cancer, it was evaluated whether combining DELFT with this approach could increase the sensitivity of cancer detection (FIG. 20). An analysis of cfDNA from a subset of the treatment na

ve cancer patients using both DELFI and targeted sequencing revealed that 82% (103 of 126) of patients had fragmentation profile alterations, while 66% (83 of 126) had sequence alterations. Over 89% of cases with mutant allele fractions >1% were detected by DELFI while for cases with mutant allele fractions <1% the fraction detected by DELFI was 80%, including for cases that were undetectable using targeted sequencing (Table 7; Appendix G). When these approaches were used together, the combined sensitivity of detection increased to 91% (115 of 126 patients) with a specificity of 98% (FIG. 20).

Overall, genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals. The variability in fragment lengths and coverage in a position dependent manner throughout the genome may explain the apparently contradictory observations of previous analyses of cfDNA at specific loci or of overall fragment sizes. In patients with cancer, heterogeneous fragmentation patterns in cfDNA appear to be a result of mixtures of nucleosomal DNA from both blood and neoplastic cells. These studies provide a method for simultaneous analysis of tens to potentially hundreds of tumor-specific abnormalities from minute amounts of cfDNA, overcoming a limitation that has precluded the possibility of more sensitive analyses of cfDNA. DELFI analyses detected a higher fraction of cancer patients than previous cfDNA analysis methods that have focused on sequence or overall fragmentation sizes (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415; Cohen et al., 2018 Science 359:926; Newman et al., 2014 Nat Med 20:548; Bettegowda et al., 2014 Sci Transl Med 6:224ra24; Newman et al., 2016 Nat Biotechnol 34:547). As demonstrated in this Example, combining DELFI with analyses of other cfDNA alterations may further increase the sensitivity of detection. As fragmentation profiles appear related to nucleosomal DNA patterns, DELFI may be used for determining the primary source of tumor-derived cfDNA. The identification of the source of circulating tumor DNA in over half of patients analyzed may be further improved by including clinical characteristics, other biomarkers, including methylation changes, and additional diagnostic approaches (Ruibal Morell, 1992 The International journal of biological markers 7:160; Galli et al., 2013 Clinical chemistry and laboratory medicine 51:1369; Sikaris, 2011 Heart, lung & circulation 20:634; Cohen et al., 2018 Science 359:926). Finally, this approach requires only a small amount of whole genome sequencing, without the need for deep sequencing typical of approaches that focus on specific alterations. The performance characteristics and limited amount of sequencing needed for DELFI suggests that our approach could be broadly applied for screening and management of patients with cancer.

These results demonstrate that genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals. As such, cfDNA fragmentation profiles can have important implications for future research and applications of non-invasive approaches for detection of human cancer.

OTHER EMBODIMENTS

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

TABLE 1 APPENDIX A: Summary of patients and samples analyzed Whole Volume Genome Targeted Age at Site of Histo- Degree of Location of of cfDNA cfDNA Fragment Fragment Targeted Patient Sample Diag- TNM Primary pathological Differenti- Metastases Plasma Extracted Input Profile Profile Mutation Patient Type Type Timepoint nosis Gender Stage Staging Tumor Diagnosis ation at Diagnosis (ml) (ng/ml) (ng/ml) Analysis Analysis Analysis CGCRC291 Colorectal cfDNA Preoperative 69 F IV T3N2M1 Coecum Adenocarcinoma Moderate Synchronous  7.9    7.80   7.80 Y Y Y Cancer treatment Liver na 

ve CGCRC292 Colorectal cfDNA Preoperative 51 M IV T3N2M1 Sigmoid Adenocarcinoma Moderate Synchronous  7.9    6.73   6.73 Y Y Y Cancer treatment Colon Liver na 

ve CGCRC293 Colorectal cfDNA Preoperative 55 M IV T3N2M1 Rectum Adenocarcinoma Moderate Synchronous  7.2    3.83   3.83 Y Y Y Cancer treatment Liver na 

ve CGCRC294 Colorectal cfDNA Preoperative 67 F II T3N0M0 Sigmoid Adenocarcinoma Moderate None  8.4   18.87  18.87 Y Y Y Cancer treatment Colon na 

ve CGCRC296 Colorectal cfDNA Preoperative 76 F II T4N0M0 Coecum Adenocarcinoma Poor None  4.3   31.24  31.24 Y Y Y Cancer treatment na 

ve CGCRC299 Colorectal cfDNA Preoperative 71 M I T1N0M0 Rectum Adenocarcinoma Moderate None  8.8   10.18  10.18 Y Y Y Cancer treatment na 

ve CGCRC300 Colorectal cfDNA Preoperative 65 M I T2N0M0 Rectum Adenocarcinoma Moderate None  4.3  1048  10.48 Y Y Y Cancer treatment na 

ve CGCRC301 Colorectal cfDNA Preoperative 76 F I T2N0M0 Rectum Adenocarcinoma Moderate None  4.1    6.51   6.51 Y Y Y Cancer treatment na 

ve CGCRC302 Colorectal cfDNA Preoperative 73 M II T3N0M0 Transverse Adenocarcinoma Moderate None  4.3   52.13  52.13 Y Y Y Cancer treatment Colon na 

ve CGCRC304 Colorectal cfDNA Preoperative 86 F II T3N0M0 Rectum Adenocarcinoma Moderate None  4.1   30.19  30.19 Y Y Y Cancer treatment na 

ve CGCRC305 Colorectal cfDNA Preoperative 83 F II T3N0M0 Transverse Adenocarcinoma Moderate None  8.6    9.10   9.10 Y Y Y Cancer treatment Colon na 

ve CGCRC306 Colorectal cfDNA Preoperative 80 F II T4N0M0 Ascending Adenocarcinoma Moderate None  4.5  2431  24.31 Y Y Y Cancer treatment Colon na 

ve CGCRC307 Colorectal cfDNA Preoperative 78 F II T3N0M0 Ascending Adenocarcinoma Moderate None  8.5   14.26  14.26 Y Y Y Cancer treatment Colon na 

ve CGCRC308 Colorectal cfDNA Preoperative 72 F III T4N2M0 Ascending Adenocarcinoma Moderate None  4.3   46.87  46.87 Y Y Y Cancer treatment Colon na 

ve CGCRC311 Colorectal cfDNA Preoperative 59 M I T2N0M0 Sigmoid Adenocarcinoma Moderate None  8.5    3.91   3.91 Y Y Y Cancer treatment Colon na 

ve CGCRC315 Colorectal cfDNA Preoperative 74 M III T3N1M0 Sigmoid Adenocarcinoma Moderate None  8.6    9.67   9.67 Y Y Y Cancer treatment Colon na 

ve CGCRC316 Colorectal cfDNA Preoperative 80 M III T3N2M0 Transverse Adenocarcinoma Moderate None  4.9   52.16  52.16 Y Y Y Cancer treatment Colon na 

ve CGCRC317 Colorectal cfDNA Preoperative 74 M III T3N2M0 Descending Adenocarcinoma Moderate None  8.8   16.08  16.08 Y Y Y Cancer treatment Colon na 

ve CGCRC318 Colorectal cfDNA Preoperative 81 M I T2N0M0 Coecum Adenocarcinoma Moderate None  9.8   18.24  18.24 Y Y Y Cancer treatment na 

ve CGCRC319 Colorectal cfDNA Preoperative 80 F III T2N1M0 Descending Adenocarcinoma Moderate None  4.2   53.54  53.54 Y N Y Cancer treatment Colon na 

ve CGCRC320 Colorectal cfDNA Preoperative 73 F I T2N0M0 Ascending Adenocarcinoma Moderate None  4.5  3037  30.37 Y Y Y Cancer treatment Colon na 

ve CGCRC321 Colorectal cfDNA Preoperative 68 M I T2N0M0 Rectum Adenocarcinoma Moderate None  9.3    4.25   4.25 Y Y Y Cancer treatment na 

ve CGCRC333 Colorectal cfDNA Preoperative NA F IV NA Colon/ Adenocarcinoma NA Liver  4.0  113.88  113.88 Y Y Y Cancer treatment Rectum na 

ve CGCRC336 Colorectal cfDNA Preoperative NA M IV NA Colon/ Adenocarcinoma NA Liver  4.4 211/4  211.74 Y Y Y Cancer treatment Rectum na 

ve CGCRC338 Colorectal cfDNA Preoperative NA F IV NA Colon/ Adenocarcinoma NA Liver  2.3 109/6  109.76 Y Y Y Cancer treatment Rectum na 

ve CGCRC341 Colorectal cfDNA Preoperative NA F IV NA Colon/ Adenocarcinoma NA Liver  4.6  156.62  156.62 Y N Y Cancer treatment Rectum na 

ve CGCRC342 Colorectal cfDNA Preoperative NA M IV NA Colon/ Adenocarcinoma NA Liver  3.9   56.09  56.09 Y N Y Cancer treatment Rectum na 

ve CGLU316 Lung cfDNA Pre-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung  5.0    2.38   2.38 Y N Y Cancer Day −53 Lobe of Lung Squamous, Small Cell Carcinoma CGLU316 Lung cfDNA Pre-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung  5.0    2.11   2.11 Y N Y Cancer Day −4 Lobe of Lung Squamous, Small Cell Carcinoma CGLU316 Lung cfDNA Post-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung  5.0    0.87   1.07 Y N Y Cancer Day 18 Lobe of Lung Squamous, Small Cell Carcinoma CGLU316 Lung cfDNA Post-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung  2.0    8.74   8.75 Y N Y Cancer Day 87 Lobe of Lung Squamous, Small Cell Carcinoma CGLU344 Lung cfDNA Pre-treatment, 65 F IV T2N2M1 Right Upper Adenocarcinoma NA Pleura,  5.0   34.77  25.00 Y N Y Cancer Day −21 Lobe of Lull, Liver, Peritoneum CGLU344 Lung cfDNA Pre-treatment, 65 F IV T2N2M1 Right Upper Adenocarcinoma NA Pleura,  5.0   15.63  15.64 Y N Y Cancer Day 0 Lobe of Lull, Liver, Peritoneum CGLU344 Lung cfDNA Post-treatment, 65 F IV T2N2M1 Right Upper Adenocarcinoma NA Pleura,  5.0    9.22   9.22 Y N Y Cancer Day 0.1875 Lobe of Lull, Liver, Peritoneum CGLU344 Lung cfDNA Post-treatment, 65 F IV T2N2M1 Right Upper Adenocarcinoma NA Pleura,  5.0    5.31   5.32 Y N Y Cancer Day 59 Lobe of Lull, Liver, Peritoneum CGLU369 Lung cfDNA Pre-treatment, 48 F IV T2NxM1 Right Upper Adenocarcinoma NA Brain  2.0   11.28  11.28 Y N Y Cancer Day −2 Lobe of Lull, CGLU369 Lung cfDNA Post-treatment, 48 F IV T2NxM1 Right Upper Adenocarcinoma NA Brain  5.0   10.09  10.09 Y N Y Cancer Day 12 Lobe of Lull, CGLU369 Lung cfDNA Post-treatment, 48 F IV T2NxM1 Right Upper Adenocarcinoma NA Brain  5.0    6.69   6.70 Y N Y Cancer Day 68 Lobe of Lull, CGLU369 Lung cfDNA Post-treatment, 48 F IV T2NxM1 Right Upper Adenocarcinoma NA Brain  5.0    8.41   8.42 Y N Y Cancer Day 110 Lobe of Lull, CGLU373 Lung cfDNA Pre-treatment, 56 F IV T3N1M0 Right Upper Adenocarcinoma Moderate None  5.0    6.35   6.35 Y N Y Cancer Day −2 Lobe of Lull, CGLU373 Lung cfDNA Post-treatment, 56 F IV T3N1M0 Right Upper Adenocarcinoma Moderate None  5.0    6.28   6.28 Y N Y Cancer Day 0.125 Lobe of Lull, CGLU373 Lung cfDNA Post-treatment, 56 F IV T3N1M0 Right Upper Adenocarcinoma Moderate None  5.0    3.82   3.82 Y N Y Cancer Day 7 Lobe of Lull, CGLU373 Lung cfDNA Post-treatment, 56 F IV T3N1M0 Right Upper Adenocarcinoma Moderate None  3.5    5.55   5.55 Y N Y Cancer Day 47 Lobe of Lull, CGPLBR100 Breast cfDNA Preoperative 44 F III T2N2M0 Left Breast Infiltrating NA None  4.0    4.25   4.25 Y N Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR101 Breast cfDNA Preoperative 46 F II T2N1M0 Left Breast Infiltrating Moderate None  4.0   37.88  37.88 Y N Y Cancer treatment Lobular na 

ve Carcinoma CGPLBR102 Breast cfDNA Preoperative 47 F II T2N1M0 Right Breast Infiltrating Moderate None  3.6   13.67  13.67 Y N Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR103 Breast cfDNA Preoperative 48 F II T2N1M0 Left Breast Infiltrating Moderate None  3.6    7.11   7.11 Y N Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR104 Breast cfDNA Preoperative 68 F II T2N0M0 Right Breast Infiltrating Moderate None  4.7   19.89  19.89 Y N Y Cancer treatment Lobular na 

ve Carcinoma CGPLBR12 Breast cfDNA Preoperative NA F III NA Breast Ductal NA NA  4.3    4.21   4.21 Y N N Cancer treatment Carcinoma na 

ve insitu with Microinvasion CGPLBR18 Breast cfDNA Preoperative NA F III NA Breast Infiltrating NA NA  4.1  4039  30.49 Y N N Cancer treatment Lobular na 

ve Carcinoma CGPLBR23 Breast cfDNA Preoperative 53 F II NA Breast Infiltrating NA None  4.7   20.09  20.09 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR24 Breast cfDNA Preoperative 53 F II NA Breast Infiltrating NA None  3.6  5833  34.72 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR28 Breast cfDNA Preoperative 59 F III NA Breast Infiltrating NA None  4.2   12.86  12.86 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR30 Breast cfDNA Preoperative 61 F II NA Breast Infiltrating NA None  4.1 59/3  30.49 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR31 Breast cfDNA Preoperative 54 F II NA Breast Infiltrating NA None  3.4   23.94  23.94 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR32 Breast cfDNA Preoperative NA F II NA Breast Infiltrating NA None  4.4   71.23  28.41 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR33 Breast cfDNA Preoperative 47 F II NA Breast Infiltrating NA None  4.4   11.00  11.00 Y N N Cancer treatment Lobular na 

ve Carcinoma CGPLBR34 Breast cfDNA Preoperative 60 F II NA Breast Infiltrating NA None  4.4   23.61  23.61 Y N N Cancer treatment Lobular na 

ve Carcinoma CGPLBR35 Breast cfDNA Preoperative 43 F II NA Breast Ductal NA None  4.5   22.58  22.58 Y N N Cancer treatment Carcinoma na 

ve insitu with Microinvasion CGPLBR36 Breast cfDNA Preoperative 36 F II NA Breast Infiltrating NA None  4.4 17/3  17.73 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR37 Breast cfDNA Preoperative 58 F II NA Breast Infiltrating NA None  4.4    9.39   9.39 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR38 Breast cfDNA Preoperative 54 F I T1N0M0 Left Breast Infiltrating Moderate None  4.0    5.77   5.77 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR40 Breast cfDNA Preoperative 66 F III T2N2M0 Left Breast Infiltrating Poor None  4.6   15.69  15.69 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR41 Breast cfDNA Preoperative 51 F III T3N1M0 Left Breast Infiltrating Moderate None  4.5   11.56  11.56 Y N Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR45 Breast cfDNA Preoperative 57 F II NA Breast Infiltrating NA None  4.5  2036  20.36 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR46 Breast cfDNA Preoperative 54 F III NA Breast Infiltrating NA None  3.5   20.17  20.17 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR47 Breast cfDNA Preoperative 54 F I NA Breast Infiltrating NA None  4.5   13.89  13.89 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR48 Breast cfDNA Preoperative 47 F II T2N1M0 Left Breast Infiltrating Poor None  3.9    7.07   7.07 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR49 Breast cfDNA Preoperative 37 F II T2N1M0 Left Breast Infiltrating Poor None  4.0    5.74   5.74 Y N Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR50 Breast cfDNA Preoperative 51 F I NA Breast Infiltrating NA None  4.5   45.58  27.78 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR51 Breast cfDNA Preoperative 53 F II NA Breast Infiltrating NA None  4.0   8.83   8.83 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR52 Breast cfDNA Preoperative 68 F III NA Breast Infiltrating NA None  4.5 80/1  27.78 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR55 Breast cfDNA Preoperative 53 F III T3N1M0 Right Breast Infiltrating Poor None  4.3    4.57   4.57 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR56 Breast cfDNA Preoperative 56 F II NA Breast Infiltrating NA None  4.5   22.16  22.16 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR57 Breast cfDNA Preoperative 54 F III T2N2M0 Left Breast Infiltrating NA None  4.3    4.02   4.02 Y N Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR59 Breast cfDNA Preoperative 42 F I T1N0M0 Left Breast Infiltrating Moderate None  4.1    8.24   8.24 Y N Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR60 Breast cfDNA Preoperative 61 F II NA Left Breast Infiltrating NA None  4.5   11.09  11.09 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR61 Breast cfDNA Preoperative 67 F II T2N1M0 Left Breast Infiltrating Moderate None  4.1   13.25  13.25 Y N Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR63 Breast cfDNA Preoperative 48 F II T2N1M0 Left Breast Infiltrating Moderate None  4.0    6.19   6.19 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR65 Breast cfDNA Preoperative 50 F II NA Left Breast Infiltrating NA None  3.5 41/5  35.71 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR68 Breast cfDNA Preoperative 64 F III T4N1M0 Breast Infiltrating Poor None  3.4   10.41  10.41 Y N Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR69 Breast cfDNA Preoperative 43 F II T2N0M0 Breast Infiltrating Moderate None  4.4   4.07   4.07 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR70 Breast cfDNA Preoperative 60 F II T2N1M0 Breast Infiltrating Moderate None  3.4   11.94  11.94 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR71 Breast cfDNA Preoperative 65 F II T2N0M0 Breast Infiltrating Poor None  3.1    7.64   7.64 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR72 Breast cfDNA Preoperative 67 F II T2N0M0 Breast Infiltrating Well None  3.9    4.43   4.43 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR73 Breast cfDNA Preoperative 60 F II T2N1M0 Breast Infiltrating Moderate None  3.3   14.69  14.69 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR76 Breast cfDNA Preoperative 53 F II T2N0M0 Right Infiltrating Well None  4.9    8.71   8.71 Y Y Y Cancer treatment Breast Ductal na 

ve Carcinoma CGPLBR81 Breast cfDNA Preoperative 54 F II NA Breast Infiltrating NA None  2.5   83.14  50.00 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR82 Breast cfDNA Preoperative 70 F I T1N0M0 Right Breast Infiltrating Moderate None  4.8  2339  23.39 Y N Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR83 Breast cfDNA Preoperative 53 F II T2N1M0 Right Breast Infiltrating Moderate None  3.7  100.17  100.17 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR84 Breast cfDNA Preoperative NA F III NA Breast Infiltrating NA NA  3.6   16.95  16.95 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR87 Breast cfDNA Preoperative 80 F II T2N1M0 Right Breast Papillary Well None  3.6 27739  69.44 Y Y Y Cancer treatment Carcinoma na 

ve CGPLBR88 Breast cfDNA Preoperative 48 F II T1N1M0 Left Breast Infiltrating Poor None  3.6 49/5  49.75 Y Y Y Cancer treatment Ductal na 

ve Carcinoma CGPLBR90 Breast cfDNA Preoperative 51 F II NA Right Breast Infiltrating NA None 31   14.24  14.24 Y N N Cancer treatment Ductal na 

ve Carcinoma CGPLBR91 Breast cfDNA Preoperative 62 F III T2N2M0 Breast Infiltrating Poor None  3.2   22.41  22.41 Y N Y Cancer treatment Lobular na 

ve Carcinoma CGPLBR92 Breast cfDNA Preoperative 58 F II T2N1M0 Breast Infiltrating Poor None  3.1  8100 8100 Y Y Y Cancer treatment Medullary na 

ve Carcinoma CGPLBR93 Breast cfDNA Preoperative 59 F II T1N0M0 Breast Infiltrating Moderate None  3.3  2714 2714 Y N Y Cancer treatment Ductal na 

ve Carcinoma CGPLH189 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 51   5.84   5.84 Y N N treatment na 

ve CGPLH190 Healthy cfDNA Preoperative 67 M NA NA NA NA NA NA  4.7  1817 1817 Y N N treatment na 

ve CGPLH192 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA  4.7   12.19  12.19 Y N N treatment na 

ve CGPLH193 Healthy cfDNA Preoperative 72 F NA NA NA NA NA NA 51    5.47   5.47 Y N N treatment na 

ve CGPLH194 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 51  918  918 Y N N treatment na 

ve CGPLH196 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA 51   11.69  11.69 Y N N treatment na 

ve CGPLH197 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 51    5.69   5.69 Y N N treatment na 

ve CGPLH198 Healthy cfDNA Preoperative 66 M NA NA NA NA NA NA 51    4.36   4.36 Y N N treatment na 

ve CGPLH199 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 51    9.77   9.77 Y N N treatment na 

ve CGPLH200 Healthy cfDNA Preoperative 51 M NA NA NA NA NA NA 51    5.60   5.60 Y N N treatment na 

ve CGPLH2O1 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 51    8.82   8.82 Y N N treatment na 

ve CGPLH2O2 Healthy cfDNA Preoperative 73 M NA NA NA NA NA NA 51    5.54   5.54 Y N N treatment na 

ve CGPLH2O3 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA 51  913  913 Y N N treatment na 

ve CGPLH2O5 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 51   4.74   4.74 Y N N treatment na 

ve CGPLH2O8 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 51    4.67   4.67 Y N N treatment na 

ve CGPLH2O9 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 51    5.15   5.15 Y N N treatment na 

ve CGPLH210 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA 51    5.41   5.41 Y N N treatment na 

ve CGPLH211 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 51    6.24   6.24 Y N N treatment na 

ve CGPLH300 Healthy cfDNA Preoperative 72 F NA NA NA NA NA NA  4.4    6.75   6.75 Y N N treatment na 

ve CGPLH307 Healthy cfDNA Preoperative 53 M NA NA NA NA NA NA  4.5    3.50   3.50 Y N N treatment na 

ve CGPLH308 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA  4.5  611  611 Y N N treatment na 

ve CGPLH309 Healthy cfDNA Preoperative 61 F NA NA NA NA NA NA  4.5    5.21   5.21 Y N N treatment na 

ve CGPLH310 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA  4.5   15.25  15.25 Y N N treatment na 

ve CGPLH311 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.5    4.47   4.47 Y N N treatment na 

ve CGPLH314 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA  4.5    9.62   9.62 Y N N treatment na 

ve CGPLH314 Healthy cfDNA, Preoperative 59 M NA NA NA NA NA NA  4.4   16.24  16.24 Y N N technical treatment replicate na 

ve CGPLH315 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA  4.2   11.55  11.55 Y N N treatment na 

ve CGPLH316 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA  4.5  2812  27.78 Y N N treatment na 

ve CGPLH317 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.5    7.62   7.62 Y N N treatment na 

ve CGPLH319 Healthy cfDNA Preoperative 60 F NA NA NA NA NA NA  4.2    4.41   4.41 Y N N treatment na 

ve CGPLH320 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA  4.5  613  613 Y N N treatment na 

ve CGPLH322 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.2    8.17   8.17 Y N N treatment na 

ve CGPLH324 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA 51    6.63   6.63 Y N N treatment na 

ve CGPLH325 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA  4.6    4.15   4.15 Y N N treatment na 

ve CGPLH326 Healthy cfDNA Preoperative 67 F NA NA NA NA NA NA  4.5  616  616 Y N N treatment na 

ve CGPLH327 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.8    1.24   1.24 Y N N treatment na 

ve CGPLH328 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA  4.4    3.42   3.42 Y N N treatment na 

ve CGPLH328 Healthy cfDNA, Preoperative 68 F NA NA NA NA NA NA 41    5.47   5.47 Y N N technical treatment replicate na 

ve CGPLH329 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA  4.5    5.27   5.27 Y N N treatment na 

ve CGPLH330 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA  4.3   10.21  10.21 Y N N treatment na 

ve CGPLH331 Healthy cfDNA Preoperative 55 M NA NA NA NA NA NA  4.6    2.63   2.63 Y N N treatment na 

ve CGPLH331 Healthy cfDNA, Preoperative 55 M NA NA NA NA NA NA  4.3    4.15   4.15 Y N N technical treatment replicate na 

ve CGPLH333 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA  4.7  416  416 Y N N treatment na 

ve CGPLH335 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA  4.4    9.39   9.39 Y N N treatment na 

ve CGPLH336 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.6    6.64   6.64 Y N N treatment na 

ve CGPLH337 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.2    4.48   4.48 Y N N treatment na 

ve CGPLH338 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA  4.5   59.44  27.78 Y N N treatment na 

ve CGPLH339 Healthy cfDNA Preoperative 70 M NA NA NA NA NA NA  4.5   12.27  12.27 Y N N treatment na 

ve CGPLH340 Healthy cfDNA Preoperative 62 M NA NA NA NA NA NA  4.5    4.86   4.86 Y N N treatment na 

ve CGPLH341 Healthy cfDNA Preoperative 61 F NA NA NA NA NA NA  4.1    7.62   7.62 Y N N treatment na 

ve CGPLH342 Healthy cfDNA Preoperative 49 F NA NA NA NA NA NA  4.2   18.29  18.29 Y N N treatment na 

ve CGPLH343 Healthy cfDNA Preoperative 58 M NA NA NA NA NA NA  4.5    3.49   3.49 Y N N treatment na 

ve CGPLH344 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.2    8.41   8.41 Y N N treatment na 

ve CGPLH345 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA  4.5    9.73   9.73 Y N N treatment na 

ve CGPLH346 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5    7.86   7.86 Y N N treatment na 

ve CGPLH35 Healthy cfDNA Preoperative 48 F NA NA NA NA NA NA 41   13.15  13.15 Y N Y treatment na 

ve CGPLH350 Healthy cfDNA Preoperative 65 M NA NA NA NA NA NA  3.5  619  619 Y N N treatment na 

ve CGPLH351 Healthy cfDNA Preoperative 71 M NA NA NA NA NA NA 41  1511 1511 Y N N treatment na 

ve CGPLH352 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.2   6.47   6.47 Y N N treatment na 

ve CGPLH353 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.2    4.47   4.47 Y N N treatment na 

ve CGPLH354 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.2   17.49  17.49 Y N N treatment na 

ve CGPLH355 Healthy cfDNA Preoperative 70 M NA NA NA NA NA NA  4.2   11.58  11.58 Y N N treatment na 

ve CGPLH356 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5  314  314 Y N N treatment na 

ve CGPLH357 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA  4.2 11/9  11.79 Y N N treatment na 

ve CGPLH358 Healthy cfDNA Preoperative 55 M NA NA NA NA NA NA  4.2  2116 2116 Y N N treatment na 

ve CGPLH36 Healthy cfDNA Preoperative 36 F NA NA NA NA NA NA 41  1310 1310 Y N Y treatment na 

ve CGPLH360 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA  4.2    3.48   3.48 Y N N treatment na 

ve CGPLH361 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.3  618  618 Y N N treatment na 

ve CGPLH362 Healthy cfDNA Preoperative 72 F NA NA NA NA NA NA  4.4    8.49   8.49 Y N N treatment na 

ve CGPLH363 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA  4.5    4.44   4.44 Y N N treatment na 

ve CGPLH364 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5  1731  17.31 Y N N treatment na 

ve CGPLH365 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA  4.5    0.55   0.55 Y N N treatment na 

ve CGPLH366 Healthy cfDNA Preoperative 61 M NA NA NA NA NA NA  4.5    4.88   4.88 Y N N treatment na 

ve CGPLH367 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.4   6.48   6.48 Y N N treatment na 

ve CGPLH368 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.3   2.63   2.63 Y N N treatment na 

ve CGPLH369 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA  4.3   10.18  10.18 Y N N treatment na 

ve CGPLH369 Healthy cfDNA, Preoperative 55 F NA NA NA NA NA NA  4.4 10/1  10.71 Y N N technical treatment replicate na 

ve CGPLH37 Healthy cfDNA Preoperative 39 F NA NA NA NA NA NA 41   9.73   9.73 Y N Y treatment na 

ve CGPLH370 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5   7.22   7.22 Y N N treatment na 

ve CGPLH371 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA  4.6   5.62   5.62 Y N N treatment na 

ve CGPLH380 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.2   6.61   6.61 Y N N treatment na 

ve CGPLH381 Healthy cfDNA Preoperative 56 F NA NA NA NA NA NA  4.2  2738  27.38 Y N N treatment na 

ve CGPLH382 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.5   11.58  11.58 Y N N treatment na 

ve CGPLH383 Healthy cfDNA Preoperative 62 F NA NA NA NA NA NA  4.5   25.50  25.50 Y N N treatment na 

ve CGPLH384 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.5  1516  15.66 Y N N treatment na 

ve CGPLH385 Healthy cfDNA Preoperative 69 M NA NA NA NA NA NA  4.5  1935  19.35 Y N N treatment na 

ve CGPLH386 Healthy cfDNA Preoperative 62 M NA NA NA NA NA NA  4.5   6.46   6.46 Y N N treatment na 

ve CGPLH386 Healthy cfDNA, Preoperative 62 M NA NA NA NA NA NA  4.6   6.54   6.54 Y N N technical treatment replicate na 

ve CGPLH387 Healthy cfDNA Preoperative 71 F NA NA NA NA NA NA  4.5   6.19   6.19 Y N N treatment na 

ve CGPLH388 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA  4.5   6.62   6.62 Y N N treatment na 

ve CGPLH389 Healthy cfDNA Preoperative 73 F NA NA NA NA NA NA  4.6 14/8  14.78 Y N N treatment na 

ve CGPLH390 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5   12.14  12.14 Y N N treatment na 

ve CGPLH391 Healthy cfDNA Preoperative 58 M NA NA NA NA NA NA  4.5   8.88   8.88 Y N N treatment na 

ve CGPLH391 Healthy cfDNA, Preoperative 58 M NA NA NA NA NA NA  4.5   8.37   8.37 Y N N technical treatment replicate na 

ve CGPLH392 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA  4.5   8.39   8.39 Y N N treatment na 

ve CGPLH393 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA  4.5   5.27   5.27 Y N N treatment na 

ve CGPLH394 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA  4.4   3.79   3.79 Y N N treatment na 

ve CGPLH395 Healthy cfDNA Preoperative 56 F NA NA NA NA NA NA  4.4   9.56   9.56 Y N N treatment na 

ve CGPLH395 Healthy cfDNA, Preoperative 56 F NA NA NA NA NA NA  4.4   5.40   5.40 Y N N technical treatment replicate na 

ve CGPLH396 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.4  2031  20.31 Y N N treatment na 

ve CGPLH398 Healthy cfDNA Preoperative 68 M NA NA NA NA NA NA  4.3   13.01  13.01 Y N N treatment na 

ve CGPLH399 Healthy cfDNA Preoperative 62 F NA NA NA NA NA NA  4.4   4.79   4.79 Y N N treatment na 

ve CGPLH400 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA  4.4   7.70   7.70 Y N N treatment na 

ve CGPLH400 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA  4.4   6.26   6.26 Y N N treatment na 

ve CGPLH401 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.3   13.01  13.01 Y N N treatment na 

ve CGPLH401 Healthy cfDNA, Preoperative 50 M NA NA NA NA NA NA  4.4   11.13  11.13 Y N N technical treatment replicate na 

ve CGPLH402 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA  4.5   2.89   2.89 Y N N treatment na 

ve CGPLH403 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA  4.3   4.41   4.41 Y N N treatment na 

ve CGPLH404 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.2   6.38   6.38 Y N N treatment na 

ve CGPLH405 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.4   7.28   7.28 Y N N treatment na 

ve CGPLH406 Healthy cfDNA Preoperative 57 M NA NA NA NA NA NA  4.2   5.40   5.40 Y N N treatment na 

ve CGPLH407 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA  4.0  1330  13.30 Y N N treatment na 

ve CGPLH408 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.2   5.18   5.18 Y N N treatment na 

ve CGPLH409 Healthy cfDNA Preoperative 53 M NA NA NA NA NA NA  3.7   3.98   3.98 Y N N treatment na 

ve CGPLH410 Healthy cfDNA Preoperative 52 M NA NA NA NA NA NA  4.1   6.91   6.91 Y N N treatment na 

ve CGPLH411 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.1   3.30   3.30 Y N N treatment na 

ve CGPLH412 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.1   5.55   5.55 Y N N treatment na 

ve CGPLH413 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA  4.5   8.18   8.18 Y N N treatment na 

ve CGPLH414 Healthy cfDNA Preoperative 56 M NA NA NA NA NA NA  3.8   5.85   5.85 Y N N treatment na 

ve CGPLH415 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA  4.7   10.20  10.20 Y N N treatment na 

ve CGPLH416 Healthy cfDNA Preoperative 58 F NA NA NA NA NA NA  4.5 11/3  11.73 Y N N treatment na 

ve CGPLH417 Healthy cfDNA Preoperative 70 M NA NA NA NA NA NA  4.2   10.98  10.98 Y N N treatment na 

ve CGPLH418 Healthy cfDNA Preoperative 70 F NA NA NA NA NA NA  4.5   10.96  10.96 Y N N treatment na 

ve CGPLH419 Healthy cfDNA Preoperative 65 F NA NA NA NA NA NA  4.5   10.17  10.17 Y N N treatment na 

ve CGPLH42 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA  4.0  1430  14.30 Y N Y treatment na 

ve CGPLH420 Healthy cfDNA Preoperative 51 M NA NA NA NA NA NA  4.2  1232  12.32 Y N N treatment na 

ve CGPLH422 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA  4.6    5.42   5.42 Y N N treatment na 

ve CGPLH423 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA  4.2    2.85   2.85 Y N N treatment na 

ve CGPLH424 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.7    1.66   1.66 Y N N treatment na 

ve CGPLH425 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.4    5.98   5.98 Y N N treatment na 

ve CGPLH426 Healthy cfDNA Preoperative 68 M NA NA NA NA NA NA  4.4    2.84   2.84 Y N N treatment na 

ve CGPLH427 Healthy cfDNA Preoperative 68 M NA NA NA NA NA NA  4.4   10.86  10.86 Y N N treatment na 

ve CGPLH428 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5    6.27   6.27 Y N N treatment na 

ve CGPLH429 Healthy cfDNA Preoperative 63 F NA NA NA NA NA NA  4.5    3.89   3.89 Y N N treatment na 

ve CGPLH43 Healthy cfDNA Preoperative 49 F NA NA NA NA NA NA  4.0    8.50   8.50 Y N Y treatment na 

ve CGPLH430 Healthy cfDNA Preoperative 69 F NA NA NA NA NA NA  4.2  1033  10.33 Y N N treatment na 

ve CGPLH431 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA  4.8   12.81  12.81 Y N N treatment na 

ve CGPLH432 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA  4.8    2.42   2.42 Y N N treatment na 

ve CGPLH434 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA  4.6    8.83   8.83 Y N N treatment na 

ve CGPLH435 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.5    8.95   8.95 Y N N treatment na 

ve CGPLH436 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5    4.29   4.29 Y N N treatment na 

ve CGPLH437 Healthy cfDNA Preoperative 56 M NA NA NA NA NA NA  4.6   18.07  18.07 Y N N treatment na 

ve CGPLH438 Healthy cfDNA Preoperative 69 M NA NA NA NA NA NA  4.8   16.62  16.62 Y N N treatment na 

ve CGPLH439 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.7    4.38   4.38 Y N N treatment na 

ve CGPLH440 Healthy cfDNA Preoperative 72 M NA NA NA NA NA NA  4.7    4.32   4.32 Y N N treatment na 

ve CGPLH441 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.7    7.80   7.80 Y N N treatment na 

ve CGPLH442 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA  4.5    6.15   6.15 Y N N treatment na 

ve CGPLH443 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA  4.4    3.44   3.44 Y N N treatment na 

ve CGPLH444 Healthy cfDNA Preoperative 60 F NA NA NA NA NA NA  4.4    4.12   4.12 Y N N treatment na 

ve CGPLH445 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.4    4.38   4.38 Y N N treatment na 

ve CGPLH446 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA  4.4    2.92   2.92 Y N N treatment na 

ve CGPLH447 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.6    3.87   3.87 Y N N treatment na 

ve CGPLH448 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA  4.4    5.29   5.29 Y Y N treatment na 

ve CGPLH449 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA  4.5    3.77   3.77 Y N N treatment na 

ve CGPLH45 Healthy cfDNA Preoperative 58 F NA NA NA NA NA NA  4.0   10.85   0.85 Y N Y treatment na 

ve CGPLH450 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5    5.62   5.62 Y N N treatment na 

ve CGPLH451 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA  4.6    7.24   7.24 Y N N treatment na 

ve CGPLH452 Healthy cfDNA Preoperative 69 M NA NA NA NA NA NA  4.4    2.54   2.54 Y N N treatment na 

ve CGPLH453 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.6    9.11   9.11 Y N N treatment na 

ve CGPLH455 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA  4.4    2.64   2.64 Y N N treatment na 

ve CGPLH455 Healthy cfDNA, Preoperative 55 F NA NA NA NA NA NA  4.5    2.42   2.42 Y N N technical treatment replicate na 

ve CGPLH456 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA  4.5    3.11   3.11 Y N N treatment na 

ve CGPLH457 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA  4.4    5.92   5.92 Y N N treatment na 

ve CGPLH458 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA  4.5   16.04   6.04 Y N N treatment na 

ve CGPLH459 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.4    6.52   6.52 Y N N treatment na 

ve CGPLH46 Healthy cfDNA Preoperative 35 F NA NA NA NA NA NA  4.0    8.25   8.25 Y N Y treatment na 

ve CGPLH460 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.6    5.24   5.24 Y N N treatment na 

ve CGPLH463 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.5 22/7  22.77 Y N N treatment na 

ve CGPLH464 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.4    2.90   2.90 Y N N treatment na 

ve CGPLH465 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5    4.76   4.76 Y N N treatment na 

ve CGPLH466 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.6    5.68   5.68 Y N N treatment na 

ve CGPLH466 Healthy cfDNA, Preoperative 50 F NA NA NA NA NA NA  4.5    6.75   6.75 Y N N technical treatment replicate na 

ve CGPLH467 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5    4.59   4.59 Y N N treatment na 

ve CGPLH468 Healthy cfDNA Preoperative 53 M NA NA NA NA NA NA  4.5   11.19  11.19 Y N N treatment na 

ve CGPLH469 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5    3.25   3.25 Y N N treatment na 

ve CGPLH47 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.0    7.43   7.43 Y N Y treatment na 

ve CGPLH470 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA  4.5   13.64  13.64 Y N N treatment na 

ve CGPLH471 Healthy cfDNA Preoperative 70 F NA NA NA NA NA NA  4.3   13.00  13.00 Y N N treatment na 

ve CGPLH472 Healthy cfDNA Preoperative 69 F NA NA NA NA NA NA  4.2   10.17  10.17 Y N N treatment na 

ve CGPLH473 Healthy cfDNA Preoperative 62 M NA NA NA NA NA NA  4.3    2.98   2.98 Y N N treatment na 

ve CGPLH474 Healthy cfDNA Preoperative 63 M NA NA NA NA NA NA  4.3   29.15  29.15 Y N N treatment na 

ve CGPLH475 Healthy cfDNA Preoperative 67 F NA NA NA NA NA NA  4.0    7.26   7.26 Y N N treatment na 

ve CGPLH476 Healthy cfDNA Preoperative 65 F NA NA NA NA NA NA  4.3    6.16   6.16 Y N N treatment na 

ve CGPLH477 Healthy cfDNA Preoperative 61 F NA NA NA NA NA NA  4.3   15.21  15.21 Y N N treatment na 

ve CGPLH478 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA  4.4    7.29   7.29 Y N N treatment na 

ve CGPLH479 Healthy cfDNA Preoperative 52 M NA NA NA NA NA NA  4.5    8.73   8.73 Y N N treatment na 

ve CGPLH48 Healthy cfDNA Preoperative 38 F NA NA NA NA NA NA  4.0    6.38   6.38 Y N Y treatment na 

ve CGPLH480 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.4   10.62  10.62 Y N N treatment na 

ve CGPLH481 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.3    6.75   6.75 Y N N treatment na 

ve CGPLH482 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.3   23.58  23.58 Y N N treatment na 

ve CGPLH483 Healthy cfDNA Preoperative 66 M NA NA NA NA NA NA  4.4   14.44  14.44 Y N N treatment na 

ve CGPLH484 Healthy cfDNA Preoperative 72 M NA NA NA NA NA NA  4.2   1432  14.32 Y N N treatment na 

ve CGPLH485 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.3    9.64   9.64 Y N N treatment na 

ve CGPLH486 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.3   10.16  10.16 Y N N treatment na 

ve CGPLH487 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA  4.4    6.11   6.11 Y N N treatment na 

ve CGPLH488 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5    7.88   7.88 Y N N treatment na 

ve CGPLH49 Healthy cfDNA Preoperative 39 F NA NA NA NA NA NA  4.0    6.60   6.60 Y N Y treatment na 

ve CGPLH490 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5    4.18   4.18 Y N N treatment na 

ve CGPLH491 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5   13.16  13.16 Y N N treatment na 

ve CGPLH492 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA  4.5    3.83   3.83 Y N N treatment na 

ve CGPLH493 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA  4.5   25.06  25.06 Y N N treatment na 

ve CGPLH494 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.4    5.24   5.24 Y N N treatment na 

ve CGPLH495 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.4    5.03   5.03 Y N N treatment na 

ve CGPLH496 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA  4.5   34.01  27.78 Y N N treatment na 

ve CGPLH497 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA  4.5    8.24   8.24 Y N N treatment na 

ve CGPLH497 Healthy cfDNA, Preoperative 68 F NA NA NA NA NA NA  4.4    5.88   5.88 Y N N technical treatment replicate na 

ve CGPLH498 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA  4.4    5.33   5.33 Y N N treatment na 

ve CGPLH499 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA  4.5    7.85   7.85 Y N N treatment na 

ve CGPLH50 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA  4.0    7.05   7.05 Y N Y treatment na 

ve CGPLH500 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA  4.5    3.49   3.49 Y N N treatment na 

ve CGPLH501 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.3    6.29   6.29 Y N N treatment na 

ve CGPLH502 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.5    2.74   2.24 Y N N treatment na 

ve CGPLH503 Healthy cfDNA Preoperative 67 M NA NA NA NA NA NA  4.5   11.01   1.01 Y N N treatment na 

ve CGPLH504 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA  4.3    6.60   6.60 Y N N treatment na 

ve CGPLH504 Healthy cfDNA, Preoperative 57 F NA NA NA NA NA NA  4.2   10.02  10.02 Y N N technical treatment replicate na 

ve CGPLH505 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.1    5.23   5.23 Y N N treatment na 

ve CGPLH506 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA  4.5   12.23  12.23 Y N N treatment na 

ve CGPLH507 Healthy cfDNA Preoperative 56 M NA NA NA NA NA NA  4.1    9.89   9.89 Y N N treatment na 

ve CGPLH508 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA  4.5    8.68   8.68 Y N N treatment na 

ve CGPLH508 Healthy cfDNA, Preoperative 54 F NA NA NA NA NA NA  4.4    9.55   9.55 Y N N technical treatment replicate na 

ve CGPLH509 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA  4.0    9.79   9.79 Y N N treatment na 

ve CGPLH51 Healthy cfDNA Preoperative 48 F NA NA NA NA NA NA  4.0    7.85   7.85 Y N Y treatment na 

ve CGPLH510 Healthy cfDNA Preoperative 67 M NA NA NA NA NA NA  4.2   14.20  14.20 Y N N treatment na 

ve CGPLH511 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA  4.5   12.94  12.94 Y N N treatment na 

ve CGPLH512 Healthy cfDNA Preoperative 52 M NA NA NA NA NA NA  4.3    8.60   8.60 Y N N treatment na 

ve CGPLH513 Healthy cfDNA Preoperative 57 M NA NA NA NA NA NA  4.3    6.54   6.54 Y N N treatment na 

ve CGPLH514 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA  4.4   10.94  10.94 Y N N treatment na 

ve CGPLH515 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA  4.5    8.71   8.71 Y N N treatment na 

ve CGPLH516 Healthy cfDNA Preoperative 65 F NA NA NA NA NA NA  4.5    7.32   7.32 Y N N treatment na 

ve CGPLH517 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA  4.6    5.16   5.16 Y N N treatment na 

ve CGPLH517 Healthy cfDNA, Preoperative 54 F NA NA NA NA NA NA  4.5    9.74   9.74 Y N N technical treatment replicate na 

ve CGPLH518 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.4    5.92   5.92 Y N N treatment na 

ve CGPLH519 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA  4.4    6.96   6.96 Y N N treatment na 

ve CGPLH52 Healthy cfDNA Preoperative 40 F NA NA NA NA NA NA  4.0    9.90   9.90 Y N Y treatment na 

ve CGPLH520 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA  4.3    8.27   8.27 Y N N treatment na 

ve CGPLH54 Healthy cfDNA Preoperative 47 F NA NA NA NA NA NA  4.0   14.18  14.18 Y N Y treatment na 

ve CGPLH55 Healthy cfDNA Preoperative 46 F NA NA NA NA NA NA  4.0    7.35   7.35 Y N Y treatment na 

ve CGPLH56 Healthy cfDNA Preoperative 42 F NA NA NA NA NA NA  4.0    5.20   5.20 Y N Y treatment na 

ve CGPLH57 Healthy cfDNA Preoperative 39 F NA NA NA NA NA NA  4.0    7.15   7.15 Y N Y treatment na 

ve CGPLH59 Healthy cfDNA Preoperative 34 F NA NA NA NA NA NA  4.0    6.03   6.03 Y N Y treatment na 

ve CGPLH625 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.5    2.64   2.64 Y N N treatment na 

ve CGPLH625 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.5    2.69   2.69 Y N N treatment na 

ve CGPLH626 Healthy cfDNA, Preoperative 50 F NA NA NA NA NA NA  4.0   11.12  11.12 Y N N technical treatment replicate na 

ve CGPLH63 Healthy cfDNA Preoperative 47 F NA NA NA NA NA NA  4.0   10.10  10.10 Y N Y treatment na 

ve CGPLH639 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5    2.00   2.00 Y N N treatment na 

ve CGPLH64 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA  4.0    8.03   8.03 Y N Y treatment na 

ve CGPLH640 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.5    9.36   9.36 Y N N treatment na 

ve CGPLH642 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA  4.5    4.99   4.99 Y N N treatment na 

ve CGPLH643 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA  4.4    7.12   7.12 Y N N treatment na 

ve CGPLH644 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.4    5.06   5.06 Y N N treatment na 

ve CGPLH646 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA  4.4    6.75   6.75 Y N N treatment na 

ve CGPLH75 Healthy cfDNA Preoperative 46 F NA NA NA NA NA NA  4.0    3.87   3.87 Y N Y treatment na 

ve CGPLH76 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA  4.0    4.03   4.03 Y N Y treatment na 

ve CGPLH77 Healthy cfDNA Preoperative 46 F NA NA NA NA NA NA  4.0    5.89   5.89 Y N Y treatment na 

ve CGPLH78 Healthy cfDNA Preoperative 34 F NA NA NA NA NA NA  4.0    2.51   2.51 Y N Y treatment na 

ve CGPLH79 Healthy cfDNA Preoperative 37 F NA NA NA NA NA NA  4.0    3.68   3.68 Y N Y treatment na 

ve CGPLH80 Healthy cfDNA Preoperative 37 F NA NA NA NA NA NA  4.0    1.94   1.94 Y N Y treatment na 

ve CGPLH81 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA  4.0    5.16   5.16 Y N Y treatment na 

ve CGPLH82 Healthy cfDNA Preoperative 38 F NA NA NA NA NA NA  4.0    3.30   3.30 Y N Y treatment na 

ve CGPLH83 Healthy cfDNA Preoperative 60 F NA NA NA NA NA NA  4.0    5.04   5.04 Y N Y treatment na 

ve CGPLH84 Healthy cfDNA Preoperative 45 F NA NA NA NA NA NA  4.0    3.33   3.33 Y N Y treatment na 

ve CGPLLU13 Lung cfDNA Pre treatment, 72 F IV T1EN2bM1a Right Lung Adenocarcinoma NA Bone  5.0    7.67   7.67 Y N Y Cancer Day −2 CGPLLU13 Lung cfDNA Post-treatment, 72 F IV T1EN2bM1a Right Lung Adenocarcinoma NA Bone  4.5    8.39   8.39 Y N Y Cancer Day 5 CGPLLU13 Lung cfDNA Post-treatment, 72 F IV T1EN2bM1a Right Lung Adenocarcinoma NA Bone  3.2    8.66   8.66 Y N Y Cancer Day 28 CGPLLU13 Lung cfDNA Post-treatment, 72 F IV T1EN2bM1a Right Lung Adenocarcinoma NA Bone  5.0    5.97   5.97 Y N Y Cancer Day 91 CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA  2.0    2.55   2.55 Y N Y Cancer Day −38 Lobe of Lull, CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA  2.0    2.55   2.55 Y N Y Cancer Day −16 Lobe of Lull, CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA  2.0    2.55   2.55 Y N Y Cancer Day −3 Lobe of Lull, CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA  2.0    2.55   2.55 Y N Y Cancer Day 0 Lobe of Lull, CGPLLU14 Lung cfDNA Post-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA  2.0    2.55   2.55 Y N Y Cancer Day 0.33 Lobe of Lull, CGPLLU14 Lung cfDNA Post-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA  2.0    2.55   2.55 Y N Y Cancer Day 7 Lobe of Lull, CGPLLU144 Lung cfDNA Preoperative 52 M II T2aN1M0 Lung Adenocarcinoma Poor None  3.5   31.51  31.51 Y Y Y Cancer treatment na 

ve CGPLLU147 Lung cfDNA Preoperative 60 M III T3N2M0 Lung Adenosquamous Poor None  3.8    6.72  6.72 Y Y Y Cancer treatment Carcinoma na 

ve CGPLLU161 Lung cfDNA Preoperative 41 F II T3N0M0 Lung Adenocarcinoma Well None  4.0   83.04  83.04 Y N Y Cancer treatment na 

ve CGPLLU162 Lung cfDNA Preoperative 38 M II T1N1M0 Right Lung Adenocarcinoma Moderate None  3.1  4032  40.32 Y Y Y Cancer treatment na 

ve CGPLLU163 Lung cfDNA Preoperative 66 M II T1N1M0 Left Lung Adenocarcinoma Poor None  5.0   54.03  54.03 Y Y Y Cancer treatment na 

ve CGPLLU165 Lung cfDNA Preoperative 68 F II T1N1M0 Right Lung Adenocarcinoma Well None  4.5   20.13  20.13 Y Y Y Cancer treatment na 

ve CGPLLU168 Lung cfDNA Preoperative 70 F I T2aN0M0 Lung Adenocarcinoma Poor None  4.3  1938  19.38 Y Y Y Cancer treatment na 

ve CGPLLU169 Lung cfDNA Preoperative 64 M I T1bN0M0 Lung Squamous Cell Moderate None  4.2 13/0  13.70 Y N Y Cancer treatment Carcinoma na 

ve CGPLLU175 Lung cfDNA Preoperative 47 M I T2N0M0 Lung Squamous Cell Moderate None  4.4  1614  16.14 Y Y Y Cancer treatment Carcinoma na 

ve CGPLLU176 Lung cfDNA Preoperative 58 M I T2N0M0 Lung Adenosquamous Moderate None  3.2  716  716 Y Y Y Cancer treatment Carcinoma na 

ve CGPLLU177 Lung cfDNA Preoperative 45 M II T3N0M0 Right Lung Adenocarcinoma NA None 31  1917 1917 Y Y Y Cancer treatment na 

ve CGPLLU180 Lung cfDNA Preoperative 57 M I T2N0M0 Right Lung Large Cell Poor None  3.2  1931 1931 Y Y Y Cancer treatment Carcinoma na 

ve CGPLLU198 Lung cfDNA Preoperative 49 F I T2N0M0 Left Lung Adenocarcinoma Moderate None  4.2  1419 1419 Y Y Y Cancer treatment na 

ve CGPLLU202 Lung cfDNA Preoperative 68 M I T2N0M0 Right Lung Adenocarcinoma NA None  4.4 24/2  24.72 Y Y Y Cancer treatment na 

ve CGPLLU203 Lung cfDNA Preoperative 66 M II T3N0M0 Right Lung Squamous Cell Well None  4.2   26.24  26.24 Y N Y Cancer treatment Carcinoma na 

ve CGPLLU205 Lung cfDNA Preoperative 65 M II T3N0M0 Left Lung Adenocarcinoma Poor None 41  1816 18/6 Y Y Y Cancer treatment na 

ve CGPLLU206 Lung cfDNA Preoperative 55 M III T3N1M0 Right Lung Squamous Cell Poor None 3/   18.24  18.24 Y Y Y Cancer treatment Carcinoma na 

ve CGPLLU207 Lung cfDNA Preoperative 60 F II T2N1M0 Lung Adenocarcinoma Well None 41   17.29  17.29 Y Y Y Cancer treatment na 

ve CGPLLU208 Lung cfDNA Preoperative 56 F II T2N1M0 Lung Adenocarcinoma Moderate None 31  2434 2434 Y Y Y Cancer treatment na 

ve CGPLLU209 Lung cfDNA Preoperative 65 M II T2aN0M0 Lung Large Cell Poor None 5/  5315 5315 Y Y Y Cancer treatment Carcinoma na 

ve CGPLLU244 Lung cfDNA Pre-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib,  4.5   17.84  17.48 Y N Y Cancer Day −7 Lobe of Lung Poor Brain, Pleura CGPLLU244 Lung cfDNA Pre-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib,  4.5   17.84  17.84 Y N Y Cancer Day −1 Lobe of Lung Poor Brain, Pleura CGPLLU244 Lung cfDNA Post-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib,  4.5   17.84  17.84 Y N Y Cancer Day 6 Lobe of Lung Poor Brain, Pleura CGPLLU244 Lung cfDNA Post-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib,  4.5   17.84  17.84 Y N Y Cancer Day 62 Lobe of Lung Poor Brain, Pleura CGPLLU245 Lung cfDNA Pre-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain  4.7   19.42  19.42 Y N Y Cancer Day −32 Lobe of Lung CGPLLU245 Lung cfDNA Pre-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain  4.7   19.42  19.42 Y N Y Cancer Day 0 Lobe of Lung CGPLLU245 Lung cfDNA Post-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain  4.7   19.42  19.42 Y N Y Cancer Day 7 Lobe of Lung CGPLLU245 Lung cfDNA Post-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain  4.7   19.42  19.42 Y N Y Cancer Day 21 Lobe of Lung CGPLLU246 Lung cfDNA Pre-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Pleura 5/   18.51  18.51 Y N Y Cancer Day −21 Lobe of Lung CGPLLU246 Lung cfDNA Pre-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Pleura 5/   18.51  18.51 Y N Y Cancer Day 0 Lobe of Lung CGPLLU246 Lung cfDNA Post-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Pleura 5/   18.51  18.51 Y N Y Cancer Day 9 Lobe of Lung CGPLLU246 Lung cfDNA Post-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Pleura 5/ 18/1 18/1 Y N Y Cancer Day 42 Lobe of Lung CGPLLU264 Lung cfDNA Pre-treatment, 84 M IV TAN2BM1 Left Middle Adenocarcinoma NA Lung 41  2217 2217 Y N Y Cancer Day −1 Lung CGPLLU264 Lung cfDNA Post-treatment, 84 M IV TAN2BM1 Left Middle Adenocarcinoma NA Lung  4.5   10.53  10.53 Y N Y Cancer Day 6 Lung CGPLLU264 Lung cfDNA Post-treatment, 84 M IV TAN2BM1 Left Middle Adenocarcinoma NA Lung 31    7.15   7.15 Y N Y Cancer Day 27 Lung CGPLLU264 Lung cfDNA Post-treatment, 84 M IV TAN2BM1 Left Middle Adenocarcinoma NA Lung 41  910  910 Y N Y Cancer Day 69 Lung CGPLLU265 Lung cfDNA Pre-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA Lung  4.2    7.16   7.16 Y N Y Cancer Day 0 Lobe of Lung CGPLLU265 Lung cfDNA Post-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA None 41    8.11   8.11 Y N Y Cancer Day 3 Lobe of Lung CGPLLU265 Lung cfDNA Post-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA None  4.2    7.53   7.53 Y N Y Cancer Day 7 Lobe of Lung CGPLLU265 Lung cfDNA Post-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA None 51   16.17  16.17 Y N Y Cancer Day 84 Lobe of Lung CGPLLU266 Lung cfDNA Pre-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None 51  532  532 Y N Y Cancer Day 0 Lobe of Lung CGPLLU266 Lung cfDNA Post-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None  3.5  631  631 Y N Y Cancer Day 16 Lobe of Lung CGPLLU266 Lung cfDNA Post-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None 51  714  714 Y N Y Cancer Day 83 Lobe of Lung CGPLLU266 Lung cfDNA Post-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None 51  1439 1439 Y N Y Cancer Day 328 Lobe of Lung CGPLLU267 Lung cfDNA Pre-treatment, 55 F IV T3NxM1a Right Upper Squamous Cell Poor Lung  4.5  217  217 Y N Y Cancer Day −1 Lobe of Lung Carcinoma CGPLLU267 Lung cfDNA Post-treatment, 55 F IV T3NxM1a Right Upper Squamous Cell Poor Lung  4.5  334  334 Y N Y Cancer Day 34 Lobe of Lung Carcinoma CGPLLU267 Lung cfDNA Post-treatment, 55 F IV T3NxM1a Right Upper Squamous Cell Poor Lung  3.5  310   3.00 Y N Y Cancer Day 90 Lobe of Lung Carcinoma CGPLLU269 Lung cfDNA Pre-treatment, 52 F IV T1CNxM1C Right Adenocarcinoma NA Brain, Liver,  5.0   11.40  11.40 Y N Y Cancer Day 0 Paratracheal Bone, Pleura Lesion CGPLLU269 Lung cfDNA Post-treatment, 52 F IV T1CNxM1C Right Adenocarcinoma NA Brain, Liver,  5.0    8.35   8.35 Y N Y Cancer Day 9 Paratracheal Bone, Pleura Lesion CGPLLU269 Lung cfDNA Post-treatment, 52 F IV T1CNxM1C Right Adenocarcinoma NA Brain, Liver,  3.5   17.79  17.79 Y N Y Cancer Day 28 Paratracheal Bone, Pleura Lesion CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma NA Pleura 41    4.70   4.70 Y N Y Cancer Day 259 Lobe of Lung CGPLLU271 Lung cfDNA Pre-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma NA Pleura 51  1816 1816 Y N Y Cancer Day 0 Lobe of Lung CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma NA Pleura  4.5  1314 1314 Y N Y Cancer Day 6 Lobe of Lung CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma NA Pleura  3.5   13.46  13.46 Y N Y Cancer Day 20 Lobe of Lung CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma NA Pleura 41 13/7  13.77 Y N Y Cancer Day 104 Lobe of Lung CGPLLU43 Lung cfDNA Pre-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None 41    2.17   2.17 Y N Y Cancer Day −1 Lobe of Lung CGPLLU43 Lung cfDNA Post-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None  3.7    3.26   3.26 Y N Y Cancer Day 6 Lobe of Lung CGPLLU43 Lung cfDNA Post-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None 41    4.12   4.12 Y N Y Cancer Day 27 Lobe of Lung CGPLLU43 Lung cfDNA Post-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None  3.7    8.20   8.20 Y N Y Cancer Day 83 Lobe of Lung CGPLLU86 Lung cfDNA Pre-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 41  710  710 Y N Y Cancer Day 0 Lobe of Lung CGPLLU86 Lung cfDNA Post-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 41  710  710 Y N Y Cancer Day OA Lobe of Lung CGPLLU86 Lung cfDNA Post-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 41  710  710 Y N Y Cancer Day 7 Lobe of Lung CGPLLU86 Lung cfDNA Post-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 41  710  710 Y N Y Cancer Day 17 Lobe of Lung CGPLLU88 Lung cfDNA Pre-treatment, 59 M IV NA Right Middle Adenocarcinoma NA None 51  2716  27.66 Y N Y Cancer Day 0 Lobe of Lung CGPLLU88 Lung cfDNA Post-treatment, 59 M IV NA Right Middle Adenocarcinoma NA None 51    6.49   6.49 Y N Y Cancer Day 7 Lobe of Lung CGPLLU88 Lung cfDNA Post-treatment, 59 M IV NA Right Middle Adenocarcinoma NA None 41  314  314 Y N Y Cancer Day 297 Lobe of Lung CGPLLU89 Lung cfDNA Pre-treatment, 54 F IV NA Right Upper Adenocarcinoma NA Brain, Bone,  8.0    8.43   8.43 Y N Y Cancer Day 0 Lobe of Lung Lung CGPLLU89 Lung cfDNA Post-treatment, 54 F IV NA Right Upper Adenocarcinoma NA Brain, Bone,  8.0    8.43   8.43 Y N Y Cancer Day 7 Lobe of Lung Lung CGPLLU89 Lung cfDNA Post-treatment, 54 F IV NA Right Upper Adenocarcinoma NA Brain, Bone,  8.0    8.43   8.43 Y N Y Cancer Day 22 Lobe of Lung Lung CGPLOV11 Ovarian cfDNA Preoperative 51 F IV T3cNOM1 Right Ovary Endometrioid Moderate Omentum  3.4  1735 1735 Y Y Y Cancer treatment Adenocarcinoma na 

ve CGPLOV12 Ovarian cfDNA Preoperative 45 F I T1aNOMX Ovary Endometrioid NA None  3.2   12.44  12.44 Y N Y Cancer treatment Adenocarcinoma na 

ve CGPLOV13 Ovarian cfDNA Preoperative 62 F IV T1bNOM1 Right Ovary Endometrioid Poor Omentum 31  2710 2710 Y Y Y Cancer treatment Adenocarcinoma na 

ve CGPLOV15 Ovarian cfDNA Preoperative 54 F III T3N1M0 Ovary Adenocarcinoma Poor None 51    4.77   4.77 Y Y Y Cancer treatment na 

ve CGPLOV16 Ovarian cfDNA Preoperative 40 F III T3aN0M0 Ovary Serous Moderate None  4.5   27.28  27.28 Y N Y Cancer treatment Adenocarcinoma na 

ve CGPLOV19 Ovarian cfDNA Preoperative 52 F II T2aN0M0 Ovary Endometrioid Moderate None 51   23.46  23.46 Y Y Y Cancer treatment Adenocarcinoma na 

ve CGPLOV20 Ovarian cfDNA Preoperative 52 F II T2aN0M0 Left Ovary Endometrioid Poor None  4.2    5.67   5.67 Y Y Y Cancer treatment Adenocarcinoma na 

ve CGPLOV21 Ovarian cfDNA Preoperative 51 M IV TanyN1M1 Ovary Serous Poor Omentum, 43  5632 5632 Y Y Y Cancer treatment Adenocarcinoma Appendix na 

ve CGPLOV22 Ovarian cfDNA Preoperative 64 F III T1cNXMX Left Ovary Serous Well None  4.6   17.42  17.42 Y Y Y Cancer treatment Adenocarcinoma na 

ve CGPLOV23 Ovarian cfDNA Preoperative 47 F I T1aN0M0 Ovary Serous Poor None 51 26/3  26.73 Y N Y Cancer treatment Adenocarcinoma na 

ve CGPLOV24 Ovarian cfDNA Preoperative 14 F I T1aN0M0 Ovary Germ Cell Poor None  4.2 10/1  10.71 Y N Y Cancer treatment Tumor na 

ve CGPLOV25 Ovarian cfDNA Preoperative 18 F I T1aN0M0 Ovary Germ Cell Poor None 41    6.78   6.78 Y N Y Cancer treatment Tumor na 

ve CGPLOV26 Ovarian cfDNA Preoperative 35 F I T1aN0M0 Ovary Germ Cell Poor None  4.5  2710 2710 Y N Y Cancer treatment Tumor na 

ve CGPLOV28 Ovarian cfDNA Preoperative 63 F I T1aNxM0 Right Ovary Serous NA None  3.2 10/4  10.74 Y N Y Cancer treatment Adenocarcinoma na 

ve CGPLOV31 Ovarian cfDNA Preoperative 45 F III T3aNxM0 Right Ovary Clear Cell NA None 41   14.45  14.45 Y N Y Cancer treatment adenocarcinoma na 

ve CGPLOV32 Ovarian cfDNA Preoperative 53 F I T1aNxM0 Left Ovary Mucinous NA None  3.2  2736 2736 Y N Y Cancer treatment Cystadenoma na 

ve CGPLOV37 Ovarian cfDNA Preoperative 40 F I T1cN0M0 Ovary Serous NA None  3.2  4618 4618 Y N Y Cancer treatment Carcinoma na 

ve CGPLOV38 Ovarian cfDNA Preoperative 46 F I T1cN0M0 Ovary Serous NA None  2.4   34.29  34.29 Y N Y Cancer treatment Carcinoma na 

ve CGPLOV40 Ovarian cfDNA Preoperative 53 F IV T3N0M1 Ovary Serous NA Omentum, 11 19310  156.25 Y N Y Cancer treatment Carcinoma Uterus, na 

ve Appendix CGPLOV41 Ovarian cfDNA Preoperative 57 F IV T3N0M1 Ovary Serous NA Omentum,  4.4  1013 1013 Y N Y Cancer treatment Carcinoma Uterus, na 

ve Cervix CGPLOV42 Ovarian cfDNA Preoperative 52 F I T3aN0M0 Ovary Serous NA None  4.2 49/1 49/1 Y N Y Cancer treatment Carcinoma na 

ve CGPLOV43 Ovarian cfDNA Preoperative 30 F I T1aN0M0 Ovary Mucinous NA None  4.4  919  919 Y N Y Cancer treatment Cystadeno- na 

ve carcinoma CGPLOV44 Ovarian cfDNA Preoperative 69 F I T1aN0M0 Ovary Mucinous NA None  4.5    8.79   8.79 Y N Y Cancer treatment Adenocarcinoma na 

ve CGPLOV46 Ovarian cfDNA Preoperative 58 F I T1bN0M0 Ovary Serous NA None  4.1    8.17   8.17 Y N Y Cancer treatment Carcinoma na 

ve CGPLOV47 Ovarian cfDNA Preoperative 41 F I T1aN0M0 Ovary Serous NA None  4.5  1935 1935 Y N Y Cancer treatment Cystadenoma na 

ve CGPLOV48 Ovarian cfDNA Preoperative 52 F I T1bN0M0 Ovary Serous NA None  3.5  2210 2210 Y N Y Cancer treatment Carcinoma na 

ve CGPLOV49 Ovarian cfDNA Preoperative 68 F III T3bN0M0 Ovary Serous NA None  4.2   16.48  16.48 Y N Y Cancer treatment Carcinoma na 

ve CGPLOV50 Ovarian cfDNA Preoperative 30 F III T3cN0M0 Ovary Serous NA None  4.5  819  819 Y N Y Cancer treatment Carcinoma na 

ve CGPLPA112 Pancreatic cfDNA Preoperative 58 M II NA Intra NA NA None 35   18.52  18.52 Y N N Cancer treatment Pancreatic na 

ve Bile Duct CGPLPA113 Duodenal cfDNA Preoperative 71 M I NA Intra NA NA None 41    8.24   8.24 Y N N Cancer treatment Pancreatic na 

ve Bile Duct CGPLPA114 Bile Dud cfDNA Preoperative NA F II NA Intra NA NA None 41   26.43  26.43 Y N N Cancer treatment Pancreatic na 

ve Bile Duct CGPLPA115 Bile Dud cMNA Preoperative NA M IV NA Intra NA NA NA 51   31.41  31.41 Y N N Cancer treatment Hepatic na 

ve Bile Duct CGPLPA117 Bile Dud cfDNA Preoperative NA M II NA Intra NA NA None  3.4    2.29   2.29 Y N N Cancer treatment Pancreatic na 

ve Bile Duct CGPLPA118 Bile Dud cfDNA Preoperative 68 F I NA Bile Dud Intra-Ampullary NA None  3.8    9.93   9.93 Y N Y Cancer treatment Bile Duct na 

ve CGPLPA122 Bile Dud cfDNA Preoperative 62 F II NA Bile Dud Intra-Pancreatic NA None  3.8   66.54  32.89 Y N Y Cancer treatment Bile Duct na 

ve CGPLPA124 Bile Dud cfDNA Preoperative 83 F II NA Bile Dud Intra-Ampullary moderate None  4.6   29.24  27.17 Y N Y Cancer treatment Bile Duct na 

ve CGPLPA125 Bile Dud cfDNA Preoperative 58 M II NA Bile Dud Intra-Pancreatic poor None  2.7  831  831 Y N N Cancer treatment Bile Duct na 

ve CGPLPA126 Bile Dud cfDNA Preoperative 60 M II NA Bile Dud Intra-Pancreatic NA None 43   80.56  29.07 Y N Y Cancer treatment Bile Duct na 

ve CGPLPA127 Bile Dud cfDNA Preoperative 71 F IV NA Bile Dud Extra-Pancreatic NA NA 3M   20.60 20M0 Y N N Cancer treatment Bile Duct na 

ve CGPLPA128 Bile Dud cfDNA Preoperative 67 M II NA Bile Dud Intra-Pancreatic NA None  3.9    5.91   5.91 Y N Y Cancer treatment Bile Duct na 

ve CGPLPA129 Bile Dud cfDNA Preoperative 56 F II NA Bile Dud Intra-Pancreatic NA None  4.6   27.07  27.07 Y N Y Cancer treatment Bile Duct na 

ve CGPLPA130 Bile Dud cfDNA Preoperative 82 F II NA Bile Dud Intra-Ampullary well None  4.0  434  434 Y N Y Cancer treatment Bile Duct na 

ve CGPLPA131 Bile Dud cfDNA Preoperative 71 M II NA Bile Dud Intra-Pancreatic NA None  3.9   68.95  32.05 Y N Y Cancer treatment Bile Duct na 

ve CGPLPA134 Bile Dud cfDNA Preoperative 68 M II NA Bile Dud Intra-Pancreatic NA None  4.1   58.08  30.49 Y N Y Cancer treatment Bile Duct na 

ve CGPLPA135 Bile Dud cfDNA Preoperative 67 F I NA Bile Dud Intra-Pancreatic NA NA  3.9    4.22   4.22 Y N N Cancer treatment Bile Duct na 

ve CGPLPA136 Bile Dud cfDNA Preoperative 69 F II NA Bile Dud Intra-Pancreatic NA None  4.1   20.23  20.23 Y N Y Cancer treatment Bile Duct na 

ve CGPLPA137 Bile Dud cfDNA Preoperative NA M II NA Bile Dud NA NA NA 4M    5.75   5.75 Y N N Cancer treatment na 

ve CGPLPA139 Bile Dud cfDNA Preoperative NA M IV NA Bile Dud NA NA NA  4.0  1439  14.89 Y N N Cancer treatment na 

ve CGPLPA14 Pancreatic cfDNA Preoperative 68 M II NA Pancreas Ductal Poor None  4.0  130  130 Y N N Cancer treatment Adenocarcinoma na 

ve CGPLPA140 Bile Dud cfDNA Preoperative 52 M II NA Extra Intra Pancreatic Poor None  4.7  2934  26.60 Y N Y Cancer treatment Hepatic Bile Duct na 

ve Bile Duct CGPLPA141 Bile Dud cfDNA Preoperative 68 F II NA Extra Intra Pancreatic Moderate None  2.8   53.67  44.64 Y N N Cancer treatment Hepatic Bile Duct na 

ve Bile Duct CGPLPA15 Pancreatic cfDNA Preoperative 70 F II NA Pancreas Ductal Well Lymph  4.0   1.92   1.92 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA155 Bile Dud cfDNA Preoperative NA F II NA NA NA NA NA  4.0 25/2  25.72 Y N N Cancer treatment na 

ve CGPLPA156 Pancreatic cfDNA Preoperative 73 F II NA Pancreas Ductal Poor Lymph  4.5    7.54   7.54 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA165 Bile Dud cfDNA Preoperative 42 M I NA Bile Dud Intra-Pancreatic well None  3.9   10.48  10.48 Y N N Cancer treatment Bile Duct with na 

ve Medullary Features CGPLPA168 Bile Dud cfDNA Preoperative 58 M II NA Bile Dud Extra-Pancreatic NA NA  3.0  139.12  34.72 Y N N Cancer treatment Bile Duct na 

ve CGPLPA17 Pancreatic cfDNA Preoperative 65 M II NA Pancreas Ductal Well Lymph  4.0   13.08  13.08 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA184 Bile Dud cfDNA Preoperative 75 F II NA Bile Dud Intra-Pancreatic NA None NA NA NA Y N N Cancer treatment Bile Duct na 

ve CGPLPA187 Bile Dud cfDNA Preoperative 67 F II NA Bile Dud Intra-Pancreatic NA None NA NA NA Y N N Cancer treatment Bile Duct na 

ve CGPLPA23 Pancreatic cfDNA Preoperative 58 F II NA Pancreas Ductal Moderate Lymph  4.0   16.62  16.62 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA25 Pancreatic cfDNA Preoperative 69 F II NA Pancreas Ductal Poor Lymph  4.0    8.71   8.71 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA26 Pancreatic cfDNA Preoperative 64 M II NA Pancreas Ductal Well Lymph  4.0    6.97   6.97 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA28 Pancreatic cfDNA Preoperative 79 F II NA Pancreas Ductal Well Lymph  4.0   18.13  18.13 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA33 Pancreatic cfDNA Preoperative 67 F II NA Pancreas Ductal Well Lymph  4.0 IMO IMO Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA34 Pancreatic cfDNA Preoperative 73 M II NA Pancreas Ductal Well Lymph  4.0  336  336 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA37 Pancreatic cfDNA Preoperative 67 F II NA Pancreas Ductal NA Lymph  4.0  2133  21.83 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA38 Pancreatic cfDNA Preoperative 65 M II NA Pancreas Ductal Moderate None  4.0    5.29   5.29 Y N N Cancer treatment Adenocarcinoma na 

ve CGPLPA39 Pancreatic cfDNA Preoperative 67 F II NA Pancreas Ductal Well Lymph  4.0 11/3  11.73 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA40 Pancreatic cfDNA Preoperative 64 M II NA Pancreas Ductal Well Lymph  4.0    4.78   4.78 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA42 Pancreatic cfDNA Preoperative 73 M II NA Pancreas Ductal Moderate Lymph  4.0    3.41   3.41 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA46 Pancreatic cfDNA Preoperative 59 F II NA Pancreas Ductal Poor Lymph  4.0    0.74   0.74 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA47 Pancreatic cfDNA Preoperative 67 M II NA Pancreas Ductal Well Lymph  4.0    6.01   6.01 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA48 Pancreatic cfDNA Preoperative 72 F I NA Pancreas Ductal Well None NA NA NA Y N N Cancer treatment Adenocarcinoma na 

ve CGPLPA52 Pancreatic cfDNA Preoperative 63 M II NA Pancreas Ductal Moderate None  2.5    9.86   9.86 Y N N Cancer treatment Adenocarcinoma na 

ve CGPLPA53 Pancreatic cfDNA Preoperative 46 M I NA Pancreas Ductal Poor Lymph  3.0   14.48  14.48 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA58 Pancreatic cfDNA Preoperative 74 F II NA Pancreas Ductal NA None  3.0    6.87   6.87 Y N N Cancer treatment Adenocarcinoma na 

ve CGPLPA59 Pancreatic cfDNA Preoperative 59 F II NA Pancreas Ductal Well Lymph NA NA NA Y N N Cancer treatment Adenocarcinoma Node na 

ve or Adenome CGPLPA67 Pancreatic cfDNA Preoperative 55 M III NA Pancreas Ductal Well Lymph  3.2    9.72   9.72 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA69 Pancreatic cfDNA Preoperative 70 M I NA Pancreas Ductal Well None  2.0    1.72   1.72 Y N N Cancer treatment Adenocarcinoma na 

ve CGPLPA71 Pancreatic cfDNA Preoperative 64 M II NA Pancreas Ductal Well Lymph  2.2   39.07  39.07 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA74 Pancreatic cfDNA Preoperative 71 F II NA Pancreas Ductal Moderate Lymph  2.5    4.99   4.99 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA76 Pancreatic cfDNA Preoperative 69 M II NA Pancreas Ductal Poor None  2.5   23.19  23.19 Y N N Cancer treatment Adenocarcinoma na 

ve CGPLPA85 Pancreatic cfDNA Preoperative 77 F II NA Pancreas Ductal Poor Lymph  3.0  152.46  41.67 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA86 Pancreatic cfDNA Preoperative 66 M II NA Pancreas Ductal Moderate Lymph  2.5   11.02  11.02 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA92 Pancreatic cfDNA Preoperative 72 M II NA Pancreas Ductal NA Lymph  2.0  534  534 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA93 Pancreatic cfDNA Preoperative 48 M II NA Pancreas Ductal Poor None  3.0   96.28  41.67 Y N N Cancer treatment Adenocarcinoma na 

ve CGPLPA94 Pancreatic cfDNA Preoperative 72 F II NA Pancreas Ductal NA Lymph  3.0   29.66  29.66 Y N N Cancer treatment Adenocarcinoma Node na 

ve CGPLPA95 Pancreatic cfDNA Preoperative 64 F II NA Pancreas Ductal Well Lymph NA NA NA Y N N Cancer treatment Adenocarcinoma Node na 

ve CGST102 Gastric cfDNA Preoperative 76 F II T3N0M0 Stomach Tubular Moderate None  4.1    8.03   8.03 Y N Y treatment Adenocarcinoma na 

ve CGST11 Gastric cfDNA Preoperative 49 M IV TXNXM1 Stomach Mixed Moderate None 3M 3M7 3M7 Y N N treatment Carcinoma na 

ve CGST110 Gastric cfDNA Preoperative 77 M III T4AN3aM0 Stomach Tubular Moderate None  3.8    5.00   5.00 Y N Y treatment Adenocarcinoma na 

ve CGST114 Gastric cfDNA Preoperative 65 M III T4AN1M0 Stomach Tubular Poor None  4.4  1035 1035 Y N Y treatment Adenocarcinoma na 

ve CGST13 Gastric cfDNA Preoperative 72 F II T1AN2M0 Stomach Signet Ring Cell Poor None  4.4  2433 2433 Y N Y treatment Carcinoma na 

ve CGST131 Gastric cfDNA Preoperative 63 M III T2N3aM0 Stomach Signet ring cell Poor None  4.0    4.28   4.28 Y N N treatment carcinoma na 

ve CGST141 Gastric cfDNA Preoperative 38 F III T3N2M0 Stomach Signet Ring Cell Poor None  4.4  1034  10.84 Y N Y treatment Carcinoma na 

ve CGST16 Gastric cfDNA Preoperative 78 M III T4AN3aM0 Stomach Tubular Poor None  4.0   40.69  40.69 Y N Y treatment Adenocarcinoma na 

ve CGST18 Gastric cfDNA Preoperative 56 M II T3NOM0 Stomach Mucinous Well None 43    9.78   9.78 Y N Y treatment Adenocarcinoma na 

ve CGST21 Gastric cfDNA Preoperative 39 M II T2N1 Stomach Papillary Moderate None  4.0    0.83   0.83 Y N N treatment Adenocarcinoma na 

ve CGST26 Gastric cfDNA Preoperative 51 M IV TXNXM1 Stomach Signet ring cell Poor None  3.5    5.56   5.56 Y N N treatment carcinoma na 

ve CGST28 Gastric cfDNA Preoperative 55 M X TXNXMx Stomach Undifferentiated Poor None  4.0    5.86   5.86 Y N Y treatment Carcinoma na 

ve CGST30 Gastric cfDNA Preoperative 64 F III T3N2M0 Stomach Signet Ring Cell Poor None  3.0    4.22   4.22 Y N Y treatment Carcinoma na 

ve CGST32 Gastric cfDNA Preoperative 67 M II T3N1M0 Stomach Tubular Moderate None  4.0   11.49  11.49 Y N Y treatment Adenocarcinoma na 

ve CGST33 Gastric cfDNA Preoperative 61 M I T2NOM0 Stomach Tubular Moderate None  3.5    5.71   5.71 Y N Y treatment Adenocarcinoma na 

ve CGST38 Gastric cfDNA Preoperative 71 F 0 T0NOM0 Stomach Mucinous NA None  4.0 NA NA Y N N treatment adenocarcinoma na 

ve CGST39 Gastric cfDNA Preoperative 51 M IV TXNXMI Stomach Signet Ring Cell Poor None  3.5   20.69  20.69 Y N Y treatment Carcinoma na 

ve CGST41 Gastric cfDNA Preoperative 66 F IV TXNXMI Stomach Signet Ring Cell Poor None  3.5    7.83   7.83 Y N Y treatment Carcinoma na 

ve CGST45 Gastric cfDNA Preoperative 41 F II T3NOM0 Stomach Signet Ring Cell Poor None  3.8    7.14   7.14 Y N Y treatment Carcinoma na 

ve CGST47 Gastric cfDNA Preoperative 74 F I T1AN0M0 Stomach Tubular Moderate None  4.0    4.55   4.55 Y N Y treatment Adenocarcinoma na 

ve CGST48 Gastric cfDNA Preoperative 62 M IV TXNXM1 Stomach Tubular Poor None  4.5    8.79   8.79 Y N Y treatment Adenocarcinoma na 

ve CGST53 Gastric cfDNA Preoperative 70 M 0 T0N0M0 Stomach NA NA None  3.8  1532  15.82 Y N N treatment na 

ve CGST58 Gastric cfDNA Preoperative 58 M III T4AN3bM0 Stomach Signet Ring Cell Poor None  3.8  1931  19.81 Y N Y treatment Carcinoma na 

ve CGST67 Gastric cfDNA Preoperative 69 M I T1BN0M0 Stomach Tubular Moderate None  3.0   23.01  23.01 Y N N treatment Adenocarcinoma na 

ve CGST77 Gastric cfDNA Preoperative 70 M IV TXNXM1 Stomach Tubular Moderate None  4.5   15.09 15M9 Y N N treatment Adenocarcinoma na 

ve CGST80 Gastric cfDNA Preoperative 58 M III T3N3aM0 Stomach Mucinous Poor None  4.5    8.56   8.56 Y N Y treatment Adenocarcinoma na 

ve CGST81 Gastric cfDNA Preoperative 64 F I T2NOMx Stomach Signet Ring Cell Poor None  3.5  3732 3732 Y N Y treatment Carcinoma na 

ve CGH14 Healthy nan adult NA NA M NA NA NA NA NA NA NA NA NA Y N N elutriated CGH15 Healthy nan adult NA NA F NA NA NA NA NA NA NA NA NA Y N N elutriated NA denotes data not available or not applicable for healthy individuals.

TABLE 2 APPENDIX B: Summary of targeted cfDNA analyses Percent Fragment Bases Mapped Profile Mutation Read Bases in Bases Mapped Mapped to to Target Total Distinct Patient Patient Type Timepoint Analysis Analysis Length Target Region to Genome Target Regions Regions Coverage Coverage CGCRC291 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7501485600 3771359756 50% 44345 10359 CGCRC292 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  6736035200 3098886973 46% 36448  8603 CGCRC293 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  6300244000 2818734206 45% 33117  5953 CGCRC294 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7786872600 3911796709 50% 46016 12071 CGCRC295 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930  8240660200 3478059753 42% 40787  5826 CGCRC296 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  5718556500 2898549356 51% 33912 10180 CGCRC297 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930  7550826100 3717222432 49% 43545  5870 CGCRC298 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930 12501036400 6096393764 49% 71196  9617 CGCRC299 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7812602900 4121569690 53% 48098 10338 CGCRC300 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8648090300 3962285136 46% 46364  5756 CGCRC301 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7538758100 3695480348 49% 43024  6618 CGCRC302 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8573658300 4349420574 51% 51006 13799 CGCRC303 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  5224046400 2505714343 48% 29365  8372 CGCRC304 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  5762112600 2942170530 51% 34462 10208 CGCRC305 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7213384100 3726953480 52% 43516  8589 CGCRC306 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7075579700 3552441899 50% 41507  7372 CGCRC307 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7572687100 3492191519 46% 40793  9680 CGCRC308 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7945738000 3895908986 49% 45224 11809 CGCRC309 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8487455800 3921079811 46% 45736 10739 CGCRC310 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  9003580500 4678812441 52% 54713 11139 CGCRC311 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  6528162700 3276653864 50% 38324  6044 CGCRC312 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930  7683294300 3316719187 43% 38652  4622 CGCRC313 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930  5874099200 2896148722 49% 33821  6506 CGCRC314 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930  6883148500 3382767492 49% 39414  6664 CGCRC315 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7497252500 3775556051 50% 44034  8666 CGCRC316 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930 10684720400 5533857153 52% 64693 14289 CGCRC317 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7086877600 3669434216 52% 43538 10944 CGCRC318 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  6880041100 3326357413 48% 39077 11571 CGCRC319 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7485342900 3982677483 53% 47327 10502 CGCRC320 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7058703200 3450648135 49% 40888 10198 CGCRC321 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7203625900 3633396892 50% 43065  6499 CGCRC332 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930  7202969100 3758323705 52% 44580  3243 CGCRC333 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8767144700 4199126827 48% 49781  8336 CGCRC334 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930  7771869100 3944578280 51% 46518  5014 CGCRC335 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930  7972524600 4064901201 51% 48308  6151 CGCRC336 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8597346400 4333410573 50% 51390  7551 CGCRC337 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930  7399611700 3800666199 51% 45083  8092 CGGRC338 Colorectal Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8029493700 4179383804 52% 49380  5831 CGCRC339 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930  7938963600 4095555110 52% 48397  3808 CGCRC340 Colorectal Cancer Preoperative, Treatment na 

ve Y N 100 80930  7214889500 3706643098 51% 43805  3014 CGCRC341 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  8803159200 3668208527 42% 43106 11957 CGCRC342 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  8478811500 3425540889 40% 40328  9592 CGCRC344 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6942167800 3098232737 45% 36823  2300 CGCRC345 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  8182868200 2383173431 29% 28233  7973 CGGRC346 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7448272300 3925056341 53% 46679  5582 CGCRC347 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  5804744500 2986809912 51% 35490  4141 CGCRC349 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6943451600 3533145275 51% 41908  5762 CGCRC350 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7434818400 3848923016 52% 45678  4652 CGCRC351 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7306546400 3636910409 50% 43162  5205 CGCRC352 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7864655000 3336939252 42% 39587  4502 CGCRC353 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7501674800 3642919375 49% 43379  4666 CGCRC354 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7938270200 2379068977 30% 28256  4858 CGCRC356 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6013175900 3046754994 51% 36127  3425 CGGRC357 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6013464600 3022035300 50% 35813  4259 CGCRC358 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7227212400 3188723303 44% 37992  5286 CGCRC359 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7818567700  425110101  5%  5040  2566 CGCRC367 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6582043200 3363063597 51% 39844  5839 CGCRC368 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  8042242400 4101646000 51% 48636 11471 CGCRC370 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6940330100 3198954121 46% 38153  4826 CGCRC373 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6587201700 3120088035 47% 37234  5190 CGCRC376 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6727983100 3162416807 47% 37735  3445 CGCRC377 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6716339200 3131415570 47% 37160  4524 CGCRC378 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6523969900 2411096720 37% 28728  3239 CGCRC379 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6996252100 3371081103 48% 39999  2891 CGCRC380 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7097496300 2710244446 38% 32020  3261 CGCRC381 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6961936100 3287050681 47% 38749  9357 CGCRC382 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6959048700 2552325859 37% 30040  5148 CGCRC384 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7012798900 3293884583 47% 39158  3653 CGCRC385 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7542017900 3356570505 45% 39884  3686 CGCRC386 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6876059600 3064412286 45% 36431  2787 CGCRC387 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7399564700 3047254560 41% 36141  6675 CGCRC388 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6592692900 3137284885 48% 37285  5114 CGCRC389 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6651206300 3102100941 47% 36764  6123 CGCRC390 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  7260616800 3376667585 47% 40048  4368 CGCRC391 Colorectal Cancer Preoperative, Treatment na 

ve N Y 100 80930  6883624500 3202877881 47% 37978  5029 CGLU316 Lung Cancer Pre-treatment, Day −53 Y N 100 80930  7864415100 1991331171 25% 23601  3565 CGLU316 Lung Cancer Pre-treatment, Day −53 Y N 100 80930  7502591600 3730963390 50% 44262  3966 CGLU316 Lung Cancer Pre-treatment, Day −53 Y N 100 80930  6582515900 3187059470 48% 37813  3539 CGLU316 Lung Cancer Pre-treatment, Day −53 Y N 100 80930  6587281800 1947630979 30% 23094  4439 CGLU344 Lung Cancer Pre-treatment, Day −21 Y N 100 80930  6151628500 2748983603 45% 32462  8063 CGLU344 Lung Cancer Pre-treatment, Day −21 Y N 100 80930  7842910900 1147703178 15% 13565  4303 CGLU344 Lung Cancer Pre-treatment, Day −21 Y N 100 80930  5838083100 2291108925 39% 27067  4287 CGLU344 Lung Cancer Pre-treatment, Day −21 Y N 100 80930  7685989200 3722274529 48% 43945  3471 CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  7080245300 1271457982 18% 15109  2364 CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  7078131900 1482448715 21% 17583  4275 CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  6904701700 2124660124 31% 25230  5278 CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  7003462200 3162195578 45% 37509  6062 CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  6346267200 3053520676 48% 36137  6251 CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  6517189900 3192984468 49% 38066  8040 CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  7767146300 3572598842 46% 42378  5306 CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  7190999100 3273648804 46% 38784  4454 CGPLBR100 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  7299964400 3750278051 51% 44794  3249 CGPLBR101 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  7420822800 3810365416 51% 45565  9784 CGPLBR102 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  6679304900 3269688319 49% 38679  7613 CGPLBR103 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  7040304400 3495542468 50% 41786  6748 CGPLBR104 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  7188389200 3716096781 52% 44316  9448 CGPLBR38 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7810293900 4057576306 52% 48098  9868 CGPLBR39 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  7745701500 3805623239 49% 45084 11065 CGPLBR40 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7558990500 3652442341 48% 43333 12948 CGPLBR41 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  7900994600 3836600101 49% 45535 10847 CGPLBR44 Breast Cancer Preoperative, Treatment na 

ve Y N 100 80930  7017744200 3269110569 47% 38672  8344 CGPLBR48 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  5629044200 2611554623 46% 30860  8652 CGPLBR49 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  5784711600 2673457893 46% 31274 10429 CGPLBR55 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8309154900 4306956261 52% 51143  8328 CGPLBR57 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  8636181000 4391502618 51% 52108  5857 CGPLBR59 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  8799457700 4152328555 47% 49281  5855 CGPLBR61 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  8163706700 3952010628 48% 46755  8522 CGPLBR63 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7020533100 3542447304 50% 41956  4773 CGPLBR67 Breast Cancer Preoperative, Treatment na 

ve Y N 100 80930  8264353900 3686093696 45% 43516  7752 CGPLBR68 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  7629312300 4078969547 53% 48389  7402 CGPLBR69 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7571501500 3857354512 51% 45322  7047 CGPLBR70 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7251760700 3641333708 50% 43203  8884 CGPLBR71 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8515402600 4496696391 53% 53340  6805 CGPLBR72 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8556946900 4389761697 51% 52081  5632 CGPLBR73 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7959392300 4006933338 50% 47555  8791 CGPLBR74 Breast Cancer Preoperative, Treatment na 

ve Y N 100 80930  8524536400 4063900599 48% 48252  7013 CGPLBR75 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8260379100 3960599885 48% 46955  6319 CGPLBP76 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7774235200 3893522420 50% 46192  9628 CGPLBR77 Breast Cancer Preoperative, Treatment na 

ve Y N 100 80930  7572797600 3255963429 43% 38568  8263 CGPLBR80 Breast Cancer Preoperative, Treatment na 

ve Y N 100 80930  6845325800 3147476693 46% 37201  5595 CGPLBR82 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  8236705200 4170465005 51% 49361 12319 CGPLBR83 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7434568100 3676855019 49% 43628  5458 CGPLBR86 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7616282500 3644791327 48% 43490  7048 CGPLBR87 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  6194021300 3004882010 49% 35765  5306 CGPLBR88 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  6071567200 2847926237 47% 33945 10319 CGPLBR91 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  7192457700 3480203404 48% 41570  9912 CGPLBP92 Breast Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7678981800 3600279233 47% 42975 13580 CGPLBR93 Breast Cancer Preoperative, Treatment na 

ve N Y 100 80930  7605717800 3998713397 53% 47866 10329 CGPLBR96 Breast Cancer Preoperative, Treatment na 

ve Y N 100 80930  6297446700 2463064737 39% 29341  7937 CGPLBR97 Breast Cancer Preoperative, Treatment na 

ve Y N 100 80930  7114921600 3557069027 50% 42488 10712 CGPLH35 Healthy Preoperative, Treatment na 

ve N Y 100 80930  6919126300 2312758764 33% 25570  1989 CGPLH36 Healthy Preoperative, Treatment na 

ve N Y 100 80930  6089923400 2038548115 33% 22719  1478 CGPLH37 Healthy Preoperative, Treatment na 

ve N Y 100 80930  5557270200 1935301929 35% 21673  2312 CGPLH42 Healthy Preoperative, Treatment na 

ve N Y 100 80930  5792045400 2388036949 41% 27197  2523 CGPLH43 Healthy Preoperative, Treatment na 

ve N Y 100 80930  5568321700 2017813329 36% 23228  1650 CGPLH45 Healthy Preoperative, Treatment na 

ve N Y 100 80930  8485593200 2770176078 33% 32829  3114 CGPLH46 Healthy Preoperative, Treatment na 

ve N Y 100 80930  5083171100 1899395790 37% 21821  1678 CGPLH47 Healthy Preoperative, Treatment na 

ve N Y 100 80930  6016388500 2062392156 34% 23459  1431 CGPLH48 Healthy Preoperative, Treatment na 

ve N Y 100 80930  4958945900 1809825992 36% 20702  1698 CGPLH49 Healthy Preoperative, Treatment na 

ve N Y 100 80930  7953812200 2511365904 32% 27006  1440 CGPLH50 Healthy Preoperative, Treatment na 

ve N Y 100 80930  6989407600 2561288100 37% 29177  2591 CGPLH51 Healthy Preoperative, Treatment na 

ve N Y 100 80930  7862073200 2525091396 32% 29999  1293 CGPLH52 Healthy Preoperative, Treatment na 

ve N Y 100 80930  6939636800 2397922699 35% 27029  2501 CGPLH54 Healthy Preoperative, Treatment na 

ve N Y 100 80930 10611934700 2290823134 22% 27175  3306 CGPLH55 Healthy Preoperative, Treatment na 

ve N Y 100 80930  9912569200 2521962244 25% 27082  3161 CGPLH56 Healthy Preoperative, Treatment na 

ve N Y 100 80930  5777591900 2023874863 35% 22916  1301 CGPLH57 Healthy Preoperative, Treatment na 

ve N Y 100 80930  9234904800 1493926244 16% 15843  1655 CGPLH59 Healthy Preoperative, Treatment na 

ve N Y 100 80930  9726052100 2987875484 31% 35427  2143 CGPLH63 Healthy Preoperative, Treatment na 

ve N Y 100 80930  8696405000 2521574759 29% 26689  1851 CGPLH64 Healthy Preoperative, Treatment na 

ve N Y 100 80930  5438852600  996198502 18% 11477  1443 CGPLH75 Healthy Preoperative, Treatment na 

ve Y N 100 80930  3446444000 1505718480 44% 17805  3016 CGPLH76 Healthy Preoperative, Treatment na 

ve N Y 100 80930  7499116400 3685762725 49% 43682  4643 CGPLH77 Healthy Preoperative, Treatment na 

ve Y N 100 80930  6512408400 2537359345 39% 30280  3131 CGPLH78 Healthy Preoperative, Treatment na 

ve N Y 100 80930  7642949300 3946069680 52% 46316  5358 CGPLH79 Healthy Preoperative, Treatment na 

ve N Y 100 80930  7785475700 3910639227 50% 45280  6714 CGPLH80 Healthy Preoperative, Treatment na 

ve N Y 100 80930  7918361500 3558236955 45% 42171  5062 CGPLH81 Healthy Preoperative, Treatment na 

ve Y N 100 80930  6646268900 3112369850 47% 37119  3678 CGPLH82 Healthy Preoperative, Treatment na 

ve N Y 100 80930  7744065000 3941700596 51% 46820  5723 CGPLH83 Healthy Preoperative, Treatment na 

ve Y N 100 80930  6957686000 1447603106 21% 17280  2875 CGPLH84 Healthy Preoperative, Treatment na 

ve Y N 100 80930  8326493200 3969908122 48% 47464  3647 CGPLH86 Healthy Preoperative, Treatment na 

ve N Y 100 80930  8664194700 4470145091 52% 53398  5094 CGPLH90 Healthy Preoperative, Treatment na 

ve N Y 100 80930  7516078800 3841504088 51% 45907  4414 CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  5659546100 1721618955 30% 20587  6025 CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  6199049700 2563659840 41% 30728  6514 CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  5864396500 1194237002 20% 14331  3952 CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930  5080197700 1373550586 27% 16480  5389 CGPLLU14 Lung Cancer Pre-treatment, Day −38 N Y 100 80930  8668655700 3980731089 46% 48628  3148 CGPLLU14 Lung Cancer Pre-treatment, Day −16 N Y 100 80930  8271043600 4105092738 50% 50152  4497 CGPLLU14 Lung Cancer Pre-treatment, Day −3 N Y 100 80930  7149809200 3405754720 48% 40382  6170 CGPLLU14 Lung Cancer Pre-treatment, Day 0 N Y 100 80930  6556332200 3289504484 50% 39004  4081 CGPLLU14 Lung Cancer Post-treatment, Day 0.33 N Y 100 80930  7410378300 3464236558 47% 41108  4259 CGPLLU14 Lung Cancer Post-treatment, Day 7 N Y 100 80930  7530190700 3752054349 50% 45839  2469 CGPLLU144 Lung Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8716827400 4216576624 48% 49370 10771 CGPLLU146 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  8506844200 4195033049 49% 49084  6968 CGPLLU147 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  7416300600 3530746046 48% 41302  4691 CGPLLU161 Lung Cancer Preoperative, Treatment na 

ve N Y 100 80930  7789148700 3280139772 42% 38568 12229 CGPLLU162 Lung Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7625462000 3470147667 46% 40918 10099 CGPLLU163 Lung Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8019293200 3946533983 49% 46471 12108 CGPLLU164 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  8110030900 3592748235 44% 42161  6947 CGPLLU165 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  8389514600 4147501817 49% 48770  8996 CGPLLU168 Lung Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7690630000 3868237773 50% 45625  9711 CGPLLU169 Lung Cancer Preoperative, Treatment na 

ve N Y 100 80930  9378353000 4800407624 51% 56547 10261 CGPLLU174 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  7481844600 3067532518 41% 36321  6137 CGPLLU175 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  8532324200 4002541569 47% 47084  7862 CGPLLU176 Lung Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8143905000 4054098929 50% 47708  5588 CGPLLU177 Lung Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8421611300 4197108809 50% 49476  8780 CGPLLU178 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  8483124700 4169577489 49% 48580  6445 CGPLLU179 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  7774358700 3304915738 43% 38768  6862 CGPLLU180 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  8192813800 3937552475 48% 46498  6568 CGPLLU197 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  7996779200 3082397881 39% 36381  5388 CGPLLU198 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  7175247200 3545719100 49% 42008  6817 CGPLLU202 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  6840112800 3427820669 50% 40670  7951 CGPLLU203 Lung Cancer Preoperative, Treatment na 

ve N Y 100 80930  7468749900 3762726574 50% 44500  9917 CGPLLU204 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  7445026400 3703545153 50% 44317  6856 CGPLLU205 Lung Cancer Preoperative, Treatment na 

ve Y Y 100 80930  9205429100 4350573991 47% 51627  9810 CGPLLU206 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  7397914600 3635210205 49% 43016  7124 CGPLLU207 Lung Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7133043900 3736258011 52% 44291  8499 CGPLLU208 Lung Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7346976400 3855814032 52% 45782  8940 CGPLLU209 Lung Cancer Preoperative, Treatment na 

ve Y N 100 80930  6723337800 3362944595 50% 39531 11946 CGPLLU244 Lung Cancer Pre-treatment, Day −7 N Y 100 80930  8305560600 4182616104 50% 50851  7569 CGPLLU244 Lung Cancer Pre-treatment, Day −1 N Y 100 80930  7739951100 3788487116 49% 45925  8552 CGPLLU244 Lung Cancer Post-treatment, Day 6 N Y 100 80930  8061928000 4225322272 52% 51279  8646 CGPLLU244 Lung Cancer Post-treatment, Day 62 N Y 100 80930  8894936700 4437962639 50% 53862  7361 CGPLLU245 Lung Cancer Pre-treatment, Day −32 N Y 100 80930  7679235200 3935822054 51% 47768  7266 CGPLLU245 Lung Cancer Pre-treatment, Day 0 N Y 100 80930  8985252500 4824268339 54% 58338 10394 CGPLLU245 Lung Cancer Post-treatment, Day 7 N Y 100 80930  8518229300 4480236927 53% 54083 10125 CGPLLU245 Lung Cancer Post-treatment, Day 21 N Y 100 80930  9031131000 4824738475 53% 58313 10598 CGPLLU246 Lung Cancer Pre-treatment, Day −21 N Y 100 80930  8520360800 3509660305 41% 42349  8086 CGPLLU246 Lung Cancer Pre-treatment, Day 0 N Y 100 80930  5451467800 2826351657 52% 34243  8256 CGPLLU246 Lung Cancer Post-treatment, Day 9 N Y 100 80930  8137616600 4135036174 51% 50121  6466 CGPLLU246 Lung Cancer Post-treatment, Day 42 N Y 100 80930  8385724600 4413323333 53% 53495  7303 CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930  3254777700 3016326208 48% 36164 12138 CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930  6185331000 3087883231 50% 37003  8388 CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930  6274540300 2861143666 46% 34308  6817 CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930  5701274000 1241270938 22% 14886  4273 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  6091276800 2922585558 48% 35004  7742 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  8430107900 2945953499 46% 35219  8574 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  5869510300 2792208995 48% 33423  8423 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  5884330900 2588386038 44% 30977  9803 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  5807524900 2347651479 40% 28146  5793 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  6064269800 2086938782 34% 24994  6221 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  6785913900 3458588505 51% 41432  7765 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  6513702000 2096370387 32% 25142  6598 CGPLLU267 Lung Cancer Pre-treatment, Day −1 Y N 100 80930  6610761200 2576886619 39% 31095  4485 CGPLLU267 Lung Cancer Pre-treatment, Day −1 Y N 100 80930  6156102000 2586081726 42% 30714  5309 CGPLLU267 Lung Cancer Pre-treatment, Day −1 Y N 100 80930  6180799700 2013434756 33% 23902  3885 CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  6221168600 1499602843 24% 17799  6098 CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  5353961600 1698331125 32% 20094  5252 CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  5831612800 1521114956 26% 18067  6210 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930  6229704000 1481468974 24% 17608  4633 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930  6134366400 1351029627 22% 16170  7024 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930  6491884900 1622578435 25% 19433  5792 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930  5742881200 2349421128 41% 28171  5723 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930  5503999300 1695782705 31% 20320  5907 CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930  6575907000 3002048491 46% 35997  5445 CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930  6204350900 3016077187 49% 36162  5704 CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930  5997724300 2989608757 50% 35873  6228 CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930  6026261500 2881177658 48% 34568  7221 CGPLLU86 Lung Cancer Pre-treatment, Day 0 N Y 100 80930  8222093400 3523035056 43% 41165  3614 CGPLLU86 Lung Cancer Post-treatment, Day 0.5 N Y 100 80930  8305719500 4271264008 51% 49508  6681 CGPLLU86 Lung Cancer Post-treatment, Day 7 N Y 100 80930  6787785300 3443658418 51% 40132  3643 CGPLLU86 Lung Cancer Post-treatment, Day 17 N Y 100 80930  6213229400 3120325926 50% 36413  3560 CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  7252433900 3621678746 50% 42719  8599 CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  7679995800 4004738253 52% 46951  6387 CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930  6509178000 3316053733 51% 39274  2661 CGPLLU89 Lung Cancer Pre-treatment, Day 0 N Y 100 80930  7662496600 3781536306 49% 44097  7909 CGPLLU89 Lung Cancer Post-treatment, Day 7 N Y 100 80930  7005599500 3339612564 48% 38977  5034 CGPLLU89 Lung Cancer Post-treatment, Day 22 N Y 100 80930  8325998600 3094796789 37% 36061  2822 CGPLOV10 Ovarian Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7073534200 3402308123 48% 39820  4059 CGPLOV11 Ovarian Cancer Preoperative, Treatment na 

ve Y Y 100 80930  6924062200 3324593050 48% 38796  7185 CGPLOV12 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  6552080100 3181854993 49% 37340  6114 CGPLOV13 Ovarian Cancer Preoperative, Treatment na 

ve Y Y 100 80930  6796755500 3264897084 48% 38340  7931 CGPLOV14 Ovarian Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7856573900 3408425065 43% 39997  7712 CGPLOV15 Ovarian Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7239201500 3322285607 46% 38953  6644 CGPLOV16 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  8570755900 4344288233 51% 51009 11947 CGPLOV17 Ovarian Cancer Preoperative, Treatment na 

ve Y N 100 80930  6910310400 2805243492 41% 32828  4307 CGPLOV18 Ovarian Cancer Preoperative, Treatment na 

ve Y N 100 80930  8173037600 4064432407 50% 47714  5182 CGPLOV19 Ovarian Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7732198900 3672564399 47% 43020 11127 CGPLOV20 Ovarian Cancer Preoperative, Treatment na 

ve Y Y 100 80930  7559602000 3678700179 49% 43230  4872 CGPLOV21 Ovarian Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8949032900 4616255499 52% 54012 12777 CGPLOV22 Ovarian Cancer Preoperative, Treatment na 

ve Y Y 100 80930  8680136500 4049934586 47% 46912  9715 CGPLOV23 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  6660696600 3422631774 51% 40810  9460 CGPLOV24 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  8634287200 4272258165 49% 50736  8689 CGPLOV25 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  6978295000 3390206388 49% 40188  5856 CGPLOV26 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  7041038300 3728879661 53% 44341  8950 CGPLOV28 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  7429236900 3753051715 51% 45430  4155 CGPLOV31 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  8981384000 4621838729 51% 55429  5458 CGPLOV32 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  9344536800 4737698323 51% 57234  6165 CGPLOV37 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  8158083200 4184432898 51% 50648  6934 CGPLOV38 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  8654435400 4492987085 52% 53789  6124 CGPLOV40 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  9868640700 4934400809 50% 59049  7721 CGPLOV41 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  7689013600 38614.48829 50% 46292  4469 CGPLOV42 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  9836516300 4864154366 49% 58302  7632 CGPLOV43 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  8756507100 4515479918 52% 54661  4310 CGPLOV44 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  7576310800 4120933322 54% 49903  4969 CGPLOV46 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  9346036300 5037820346 54% 61204  3927 CGPLOV47 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930 10880620200 5491357828 50% 66363  6895 CGPLOV48 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  7658787800 3335991337 44% 40332  4066 CGPLOV49 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930 10076208000 5519656698 55% 67117  5097 CGPLOV50 Ovarian Cancer Preoperative, Treatment na 

ve N Y 100 80930  8239290400 4472380276 54% 54150  3836 CGPLPA118 Bile Duct Cancer Preoperative, Treatment na 

ve N Y 100 80930  9094827600 4828332902 53% 57021  4802 CGPLPA122 Bile Duct Cancer Preoperative, Treatment na 

ve N Y 100 80930  7303323100 3990160379 55% 47240  7875 CGPLPA124 Bile Duct Cancer Preoperative, Treatment na 

ve N Y 100 80930  7573482800 3965807442 52% 46388  8658 CGPLPA126 Bile Duct Cancer Preoperative, Treatment na 

ve N Y 100 80930  7904953600 4061463168 51% 47812 10498 CGPLPA128 Bile Duct Cancer Preoperative, Treatment na 

ve N Y 100 80930  7249238300 2244188735 31% 26436  3413 CGPLPA129 Bile Duct Cancer Preoperative, Treatment na 

ve N Y 100 80930  7559858900 4003725804 53% 47182  5733 CGPLPA130 Bile Duct Cancer Preoperative, Treatment na 

ve N Y 100 80930  6973946500 1247144905 18% 14691  1723 CGPLPA131 Bile Duct Cancer Preoperative, Treatment na 

ve N Y 100 80930  7226237900 3370664342 47% 39661  5054 CGPLPA134 Bile Duct Cancer Preoperative, Treatment na 

ve N Y 100 80930  7268866100 3754945844 52% 44306  7023 CGPLPA136 Bile Duct Cancer Preoperative, Treatment na 

ve N Y 100 80930  7476690700 4073978408 54% 48134  5244 CGPLPA140 Bile Duct Cancer Preoperative, Treatment na 

ve N Y 100 80930  7364654600 3771765342 51% 44479  7080 CGST102 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  5715504500 2644902854 46% 31309  4503 CGST110 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  9179291500 4298269268 47% 51666  3873 CGST114 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  7151572200 3254967293 46% 38496  4839 CGST13 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6449701500 3198545984 50% 38515  6731 CGST141 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6781001300 3440927391 51% 40762  5404 CGST16 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6396470600 2931380289 46% 35354  8148 CGST18 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6647324000 3138967777 47% 37401  4992 CGST28 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6288486100 2884997993 46% 34538  2586 CGST30 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6141213100 3109994564 51% 37194  2555 CGST32 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6969139300 3099120469 44% 36726  3935 CGST33 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6560309400 3168371917 48% 37916  4597 CGST39 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  7043791400 2992801875 42% 35620  6737 CGST41 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6975053100 3224065662 46% 38300  4016 CGST45 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6130812200 2944524278 48% 35264  4745 CGST47 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  5961400000 3083523351 52% 37008  3112 CGST48 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6418652700 1497230327 23% 17782  2410 CGST58 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  5818344500 1274708429 22% 15281  2924 CGST80 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  6368064600 3298497188 52% 39692  5280 CGST81 Gastric Cancer Preoperative, Treatment na 

ve N Y 100 80930  8655691400 1519121452 18% 17988  6419

TABLE 3 APPENDIX C: Targeted cfDNA fragment analyses in cancer patients Wild-type 25th Fragments Minimum Percentile Mode Median Alteration Mutant CfDNA cfDNA cfDNA efDNA Patient Stage at Alteration Amino Acid Mutation Hotspot Detected Allele Distinct Fragment Fragment Fragment Fragment Patient Type Diagnosis Type Gene (Protein) Nucleotide Type Alteration in Tissue Fraction Coverage Size (bp) Size (bp) Size (bp) Size (bp) CGCRC291 Colorectal IV Tumor- STK11 39R>C chr19_1207027-1207027_C_T Substitution No No  0.14 11688 100 151 167 169 Cancer derived CGCRC291 Colorectal IV Tumor- TP53 272V>M chr17_7577124-7577124_C_T Substitution Yes No  0.10% 11779 100 155 171 169 Cancer derived CGCRC291 Colorectal IV Tumor- TP53 167Q>X chr17_7578431-7578431_G_A Substitution Yes Yes 22.85% 11026 100 156 166 169 Cancer derived CGCRC291 Colorectal IV Tumor- KRAS 12G>A chr12_25396284-25398284_C_G Substitution Yes Yes 14.65%  7632  97 152 169 167 Cancer derived CGCRC291 Colorectal IV Tumor- APC 1260Q>X chr5_112175069-112175069_C_T Substitution No Yes 11.23%  7218 101 155 167 169 Cancer derived CGCRC291 Colorectal IV Tumor- APC 1450R>X chr5_112175639-112175639_C_T Substitution Yes Yes 11.05% 10757  86 154 166 167 Cancer derived CGCRC291 Colorectal IV Tumor- PIK3CA 542E>K chr3_178936082-178936082_G_A Substitution Yes Yes 18.11%  5429 100 151 171 167 Cancer derived CGCRC292 Colorectal IV Tumor- KRAS 146A>V chr12_25378561-25378561_G_A Substitution Yes No  1.41%  6120 101 157 167 169 Cancer derived CGCRC292 Colorectal IV Germline CTNNB1 41T>A chr3_41266124-41266124_A_G Substitution Yes Yes  0.13% 10693 100 155 169 168 Cancer CGCRC292 Colorectal IV Tumor- EGFR 2284-4C>G chr7_55248982-55248982_C_G Substitution NA Yes 31.99%  7587  97 158 166 171 Cancer derived CGCRC293 Colorectal IV Tumor- TP53 176C>S chr17_7578404-7578404_A_T Substitution No No  0.35%  7672  95 159 168 170 Cancer derived CGCRC294 Colorectal II Tumor- APC 213R>X chr5_112116592-112116592_C_T Substitution Yes Yes  0.14%  7339  84 155 166 167 Cancer derived CGCRC294 Colorectal II Tumor- APC 1367Q>X chr5_112175390-112175390_C_T Substitution Yes Yes  0.13% 12054  89 159 167 170 Cancer derived CGCRC295 Colorectal IV Tumor- PDGFRA 49+4C>T chr4_55124988-55124988_C_T Substitution No No  0.45%  5602 101 157 164 170 Cancer derived CGCRC295 Colorectal IV Germline IDH1 104G>V chr2_2091131096-209113196_ Substitution No Yes  0.34%  8330 100 157 166 169 Cancer C_A CGCRC296 Colorectal II Germline EGFR 922E>K chr7_55266472-55266472_G_A Substitution NA Yes 30.48%  8375  89 161 166 172 Cancer CGCRC297 Colorectal III Germline KIT 18L>F chr4_55524233-55524233_C_T Substitution NA Yes 41.39%  3580 102 159 164 170 Cancer CGCRC298 Colorectal II Hemato- DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution Yes Yes  0.08% 13032 100 159 168 171 Cancer poietic CGCRC298 Colorectal II Hemato- DNMT3A 7145>C chr2_25463541-25463541_G_C Substitution No No  0.11% 13475  93 158 169 170 Cancer poietic CGCRC298 Colorectal II Tumor- PIK3CA 414G>V chr3_178927478-178927478_G_T Substitution No No  0.55%  5815 100 156 168 169 Cancer derived CGCRC299 Colorectal I Hemato- DNMT3A 735Y>C chr2_25463289-25463289_T_C Substitution No Yes  0.30% 11995 100 154 164 165 Cancer poietic CGCRC299 Colorectal I Hemato- DNMT3A 710C>S chr2_25463553-25463553_C_G Substitution No Yes  0.12% 15363  96 151 166 164 Cancer poietic CGCRC300 Colorectal I Hemato- DNMT3A 720R>G chr2_25463524-25463524_G_C Substitution No No  0.15%  7487 100 162 170 173 Cancer poietic CGCRC301 Colorectal I Tumor- ATM 2397Q>X chr11_108199847-108199847_ Substitution No No  0.21%  5881 100 156 169 169 Cancer derived C_T CGCRC302 Colorectal II Tumor- TP53 141C>Y chr17_ 7578508-7578508_C_T Substitution Yes Yes  0.05% 24784  84 153 165 164 Cancer derived CGCRC302 Colorectal II Tumor- BRAF 600V>E chr7_140453136-140453136_A_T Substitution Yes Yes  0.12% 11763  95 154 165 165 Cancer derived CGCRC303 Colorectal III Tumor- TP53 173V>L chr17_7578413-7578413_C_A Substitution Yes Yes  0.08% 13967  95 159 169 171 Cancer derived CGCRC303 Colorectal III Hemato- DNMT3A 755F>S chr2_25463229-25463229_A_G Substitution No No  0.21% 10167  81 160 169 172 Cancer poietic CGCRC303 Colorectal III Hemato- DNMT3A 2173+1G>A chr2_25463508-25463508_C_T Substitution No No  0.17% 10845 100 160 169 172 Cancer poietic CGCRC304 Colorectal II Tumor- EGFR 1131T>S chr7_56273068-55273068_A_T Substitution No No  0.22% 16168  90 153 167 164 Cancer derived CGCRC304 Colorectal II Tumor- ATM 3077+1G>A chr11_108142134-108142134_ Substitution No No  0.27% 10502 100 152 165 163 Cancer derived G_A CGCRC304 Colorectal II Hemato- ATM 3008R>C chr11_108236086-108236086_ Substitution No Yes  0.43% 12987 101 154 165 165 Cancer poietic C_T CGCRC305 Colorectal II Tumor- GNA11 213R>Q chr19_3118954-3118954_G_A Substitution No Yes  0.11% 12507 100 159 169 171 Cancer derived CGCRC305 Colorectal II Tumor- TP53 273R>H chr17_7577120-7577120_C_T Substitution Yes No  0.19% 10301 100 156 168 168 Cancer derived CGCRC306 Colorectal II Tumor- TP53 196R>X chr17_7578263-7578263_G_A Substitution Yes No  0.12%  8594 101 157 165 169 Cancer derived CGCRC306 Colorectal II Tumor- CDKN2A 107R>C chr9_21971039-21971039_G_A Substitution No Yes  8.02%  9437  90 159 167 171 Cancer derived CGCRC306 Colorectal II Tumor- KRAS 61Q>K chr12_25380277-25380277_G_T Substitution Yes Yes  7.30%  6090 100 152 163 166 Cancer derived CGCRC306 Colorectal II Germline PDGFRA 200T>S chr4_55130065-55130065_C_G Substitution NA Yes 34.78%  4585 103 158 167 170 Cancer CGCRC306 Colorectal II Tumor- EGFR 618H>R chr7_55233103-55233103_A_G Substitution No Yes  6.32%  7395  81 160 166 171 Cancer derived CGCRC306 Colorectal II Tumor- PIK3CA 545E>A chr3_178936092-178936092_A_C Substitution Yes No  0.96%  4885 100 152 170 167 Cancer derived CGCRC306 Colorectal II Germline ERBB4 1156R>X chr2_212251596-212251596_G_A Substitution NA Yes 38.70%  3700 100 159 168 171 Cancer CGCRC307 Colorectal II Tumor- JAK2 805L>V chr9_5080662-5080662_C_G Substitution No No  0.56%  6860 100 158 170 170 Cancer derived CGCRC307 Colorectal II Tumor- SMARCB1 501-2A>G chr22_24145480-24145480_A_G Substitution No Yes  0.34% 10065  95 157 168 169 Cancer derived CGCRC307 Colorectal II Tumor- GNAS 201R>C chr20_57484420-57484420_C_T Substitution Yes Yes#  0.24%  7520 102 156 167 168 Cancer derived CGCRC307 Colorectal II Tumor- BRAF 600V>E chr7_140453136-140453136_A_T Substitution Yes Yes  0.38%  8623  76 157 169 168 Cancer derived CGCRC307 Colorectal II Tumor- FBXW7 465R>C chr4_153249385-153249385_G_A Substitution Yes Yes  0.31% 10606 100 155 167 168 Cancer derived CGCRC307 Colorectal II Tumor- ERBB4 17A>V chr2_213403205-213403205_G_A Substitution No No  0.15% 13189  90 156 168 171 Cancer derived CGCRC308 Colorectal III Hemato- DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution Yes No  0.06% 16287  90 159 168 169 Cancer poietic CGCRC308 Colorectal III Germline EGFR 848P>L chr7_55259485-55259485_C_T Substitution NA Yes 27.69%  7729 100 160 164 170 Cancer CGCRC308 Colorectal III Tumor- APC 1480Q>X chr5_112175729-112175729_C_T Substitution No Yes  0.11% 14067  92 157 170 169 Cancer derived CGCRC309 Colorectal III Tumor- AKT1 17E>K chr14_105246551-105246551_ Substitution Yes Yes  2.70% 13036  85 157 170 169 Cancer derived C_T CGCRC309 Colorectal III Tumor- BRAF 600V>E chr7_140453136-140453136_A_T Substitution Yes Yes  3.00%  9084 101 157 166 168 Cancer derived CGCRC310 Colorectal II Tumor- KRAS 12G>V chr12_25398284-25398284_C_A Substitution Yes Yes  0.13%  7393 100 153 165 164 Cancer derived CGCRC310 Colorectal II Tumor- APC 1513E>X chr5_112175828-112175828_G_T Substitution No Yes  0.11% 11689 100 152 166 164 Cancer derived CGCRC310 Colorectal II Tumor- APC 1521E>X chr5_112175852-112175852_G_T Substitution No Yes  0.15% 10273 100 153 166 164 Cancer derived CGCRC311 Colorectal I Hemato- DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution Yes No  0.86%  8456  94 160 171 172 Cancer poietic CGCRC312 Colorectal III Tumor- APC 9605>X chr5_112174170-112174170_C_G Substitution No Yes  0.59%  4719 100 160 165 173 Cancer derived CGCRC312 Colorectal III Tumor- NRAS 61Q>K chr1_115256630-115256530_G_T Substitution Yes Yes  0.47%  3319 101 157 172 170 Cancer derived CGCRC313 Colorectal III Tumor- KRAS 12G>S chr12_25398285-25398285_C_T Substitution Yes Yes  0.17%  5013 100 163 166 174 Cancer derived CGCRC313 Colorectal III Tumor- APC 876R>X chr5_112173917-112173917_C_T Substitution Yes Yes  0.07%  8150  72 161 171 174 Cancer derived CGCRC314 Colorectal I Tumor- KRAS 12G>D chr12_25398284-25398284_C_T Substitution Yes Yes  0.30%  4684 100 158 165 169 Cancer derived CGCRC314 Colorectal I Hemato- DNMT3A 738L>Q chr2_25463280-25463280_A_T Substitution No Yes  2.50%  6902  85 159 165 170 Cancer poietic CGCRC314 Colorectal I Tumor- APC 1379E>X chr5_112175426-112175426_G_T Substitution Yes Yes  0.38%  7229 102 158 167 170 Cancer derived CGCRC315 Colorectal III Tumor- NRAS 12G>D chr1_115258747-115258747_C_T Substitution Yes Yes  0.27%  8739  94 155 167 169 Cancer derived CGCRC315 Colorectal III Tumor- FBXW7 505R>C chr4_153247289-153247289_G_A Substitution Yes Yes  0.25%  9623 101 158 166 170 Cancer derived CGCRC316 Colorectal III Tumor- TP53 245G>S chr17_577548-7577548_C_T Substitution Yes Yes  6.52% 12880 100 150 166 163 Cancer derived CGCRC316 Colorectal III Tumor- CDKN2A 1M>R chr9_21974825-21974825_A_C Substitution No Yes  5.74%  7479  93 157 164 168 Cancer derived CGCRC316 Colorectal III Tumor- CTNNBI 37S>C chr3_41266113-41266113_C_G Substitution Yes Yes  5.47% 13682 100 149 165 162 Cancer derived CGCRC316 Colorectal III Tumor- EGFR 2702-3C>T chr7_55266407-55266407_C_T Substitution No No  0.11% 16716  85 153 166 166 Cancer derived CGCRC316 Colorectal III Hemato- ATM 3008R>P chr11_108236087-108236087_G_C Substitution No Yes  0.13% 17060 100 150 166 163 Cancer poietic CGCRC317 Colorectal III Tumor- TP53 220Y>C chr17_7578190-7578190_T_C Substitution Yes Yes  0.36% 14587  84 152 166 164 Cancer derived CGCRC317 Colorectal III Tumor- ATM 1026W>R chr11_108142132-108142132_T_C Substitution No Yes  0.23% 10483 100 152 164 165 Cancer derived CGCRC317 Colorectal III Tumor- APC 216R>X chr5_112128143-112128143_C_T Substitution Yes No  0.29%  3497 101 149 166 163 Cancer derived CGCRC318 Colorectal I Hemato- DNMT3A 698W>X chr2_25463589-25463589_C_T Substitution No Yes  0.25% 16436  98 158 170 170 Cancer poietic CGCRC320 Colorectal I Germline KIT 18L>F chr4_555243-55524233_C_T Substitution NA Yes 34.76%  6521 100 163 170 175 Cancer CGCRC320 Colorectal I Tumor- ERBB4 78R>W chr2_212989479-212989479_G_A Substitution No No  0.12% 11633 100 162 174 174 Cancer derived CGCRC321 Colorectal I Tumor- CDKN2A 12S chr9_21974792-21974792_G_A Substitution No No  0.20%  6918  88 161 167 174 Cancer derived CGCRC321 Colorectal I Hemato- DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution Yes No  0.08%  9559  94 159 171 170 Cancer poietic CGCRC321 Colorectal I Germline EGFR 51150Y chr7_55229225-55229225_C_A Substitution NA Yes 41.86%  5545 100 159 172 172 Cancer CGCRC332 Colorectal IV Tumor- TR53 125T>R Chr17_7579313-7579313_G_C Substitution No Yes 19.98%  605 104 164 170 176 Cancer derived CGCRC333 Colorectal IV Tumor- TP53 673-2A>G chr17_7577610-7577610_T_C Substitution No Yes 43.03%  1265  89 159 165 171 Cancer derived CGCRC333 Colorectal IV Tumor- BRAF 600V>E chr7_140453136-140453136_A_T Substitution Yes Yes 22.26%  3338 102 153 165 169 Cancer derived CGCRC333 Colorectal IV Tumor- ERBB4 691E>A chr2_212495194-212495194_T_G Substitution No No  1.00%  3008 102 153 169 169 Cancer derived CGCRC334 Colorectal IV Tumor- TP53 245G05 chr17_7577548-7577548_C_T Substitution Yes Yes  1344%  1725 105 160 170 175 Cancer derived CGCRC334 Colorectal IV Germline EGFR 638T>M chr7_55238900-55238900_C_T Substitution NA Yes 35.28%  1168 100 159 164 174 Cancer CGCRC334 Colorectal IV Tumor- P1K3CA 104P>R chr3_178916924-178916924_C_G Substitution No No  3.85%  1798 103 159 166 173 Cancer derived CGCRC335 Colorectal IV Tumor- BRAF 600V>E chr7_140453136-140453136_A_T Substitution Yes Yes  0.32%  2411  99 155 167 167 Cancer derived CGCRC336 Colorectal IV Tumor- TP53 175R>H chr17_7578406-7578406_C_T Substitution Yes Yes 75.26%  757 104 156 171 170 Cancer derived CGCRC336 Colorectal IV Tumor- KRAS 12G>V chr12_25398284-25398284_C_A Substitution Yes Yes 42.87%  1080 102 150 166 167 Cancer derived CGCRC336 Colorectal IV Tumor- APC 1286E>X chr5_112175147-112175147_G_T Substitution No Yes 81.61%  391 102 161 165 171 Cancer derived CGCRC337 Colorectal IV Tumor- STK11 734+2T>A chr19_1220718-1220718_T_A Substitution No No  0.12%  6497  72 153 169 177 Cancer derived CGCRC337 Colorectal IV Germline APC 485M01 chr5_112162851-112162851_G_A Substitution NA Yes 46.26%  1686 100 147 170 163 Cancer CGCRC338 Colorectal IV Tumor- KRAS 12G>D chr_12_25398284-25398284_C_T Substitution Yes Yes 27.03%  1408 105 153 164 166 Cancer derived CGCRC339 Colorectal IV Tumor- KRAS 13G>D chr12_25396281-25398281_C_T Substitution Yes Yes  1.94%  1256 106 158 168 169 Cancer derived CGCRC339 Colorectal IV Tumor- APC 876R>X chr5_112173917-112173917_C_T Substitution Yes Yes  2.35%  1639 101 158 165 172 Cancer derived CGCRC339 Colorectal IV Tumor- PIK3CA 407C>F chr3_178921457-178927457_G_T Substitution No Yes  3.14%  1143 100 154 170 167 Cancer derived CGCRC339 Colorectal IV Tumor- PIK3CA 1047H>L chr3_178952085-178952085_A_T Substitution Yes Yes  1.71%  1584 108 161 171 173 Cancer derived CGCRC340 Colorectal IV Tumor- TP53 196R>X chr17_7578263-7578263_G_A Substitution Yes Yes 18.26%  876 101 162 170 175 Cancer derived CGCRC340 Colorectal IV Tumor- APC 1306E>X chr5_112175207-112175207_G_T Substitution Yes Yes 22.57%  796 105 159 164 174 Cancer derived CGPLBR38 Breast 1 Tumor- TP53 24150P chr17_7577560-7577560_A_G Substitution No Yes  0.53%  9684  95 156 166 168 Cancer derived CGPLBR40 Breast III Germline AR 392P>R chrX_66766163-66766163_C_G Substitution NA Yes 28.99% 10277  78 162 168 173 Cancer CGPLBR44 Breast III Hemato- DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution Yes Yes  1.82% 10715  99 162 171 173 Cancer poietic CGPLBR44 Breast III Hemato- DNMT3A 7051 chr2_25463568-25463568_A_G Substitution No Yes  0.41% 10837 100 159 169 171 Cancer poietic CGPLBR44 Breast III Tumor- PDGFRA 859V>M chr4_55153609-55153609_G_A Substitution No Yes  0.13% 12640 100 159 168 171 Cancer derived CGPLBR48 Breast II Germline ALK 1231R>Q chr2_29436901-29436901_C_T Substitution NA Yes 34.61%  5631 100 164 170 179 Cancer CGPLBR48 Breast II Tumor- EGFR 669R>Q chr7_55240762-55240762_G_A Substitution No No  0.18% 12467 101 167 174 180 Cancer derived CGPLBR55 Breast III Hemato- DNMT3A 743P>S chr2_25461266-25463266_G_A Substitution No No  0.18% 10527 101 158 169 169 Cancer poietic CGPLBR55 Breast III Tumor- GNAS 201R>H chr20_57484421-57484421_G_A Substitution Yes Yes  0.68%  6011 101 153 166 167 Cancer derived CGPLBR55 Breast III Tumor- PIK3CA 345N>K chr3_178921553-178921553_T_A Substitution Yes Yes  0.42%  3973 101 153 166 166 Cancer derived CGPLBR63 Breast II Germline FGFR3 403K>E chr4_1806188-1806188_A_G Substitution NA Yes 34.82%  3405  97 165 170 176 Cancer CGPLBR67 Breast III Hemato- DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution Yes Yes  0.11% 10259  87 157 168 168 Cancer poietic CGPLBR67 Breast III Tumor- PIK3CA 545E>K chr3_178936091-178936091_G_A Substitution Yes Yes  0.68%  5163 100 151 167 166 Cancer derived CGPLBR67 Breast III Tumor- ERBB4 1000D>A chr2_212285302-212285302_T_G Substitution No No  0.28%  6250 100 155 166 167 Cancer derived CGPLBR69 Breast II Hemato- DNMT3A 774E>V chr2_25463172-25463172_T_A Substitution No No  0.29%  7558 100 159 166 170 Cancer poietic CGPLBR69 Breast II Germline CTNNBI 30Y>S chr3_41266092-41266092_A_C Substitution NA Yes 41.74%  3938 101 154 169 166 Cancer CGPLBR69 Breast II Germline IDH1 231Y>N chr2_209108158-209108158_A_T Substitution NA Yes 41.66%  2387 101 157 166 168 Cancer CGPLBR70 Breast II Tumor- ATM 2832R>H chr11_108216546-108216546_ Substitution No No  0.36%  6916 100 158 171 169 Cancer derived G_A CGPLBR70 Breast II Germline APC 1577E>D chr5_112176022-112176022_A_C Substitution NA Yes 40.28%  3580 107 160 169 173 Cancer CGPLBR71 Breast II Tumor- TP53 273R>H chr17_7577120-7577120_C_T Substitution Yes Yes  0.10%  7930  85 156 166 168 Cancer derived CGPLBR72 Breast II Germline APC 1532D>G chr5_112175886-112175886_A_G Substitution NA Yes 44.03%  2389 100 157 160 170 Cancer CGPLBR73 Breast II Tumor- ALK 7085>P chr2_29474053-29474053_A_G Substitution No No  0.27% 11348  95 161 173 174 Cancer derived CGPLBR73 Breast II Germline ERBB4 158A>E chr2_212652833-212652833_G_T Substitution NA Yes 35.58%  3422 102 157 168 169 Cancer CGPLBR74 Breast II Germline AR 20+1G>T chrX_66788865-66788865_G_T Substitution NA Yes 36.23%  3784 101 163 175 174 Cancer CGPLBR75 Breast II Tumor- PIK3CA 10471-I>R chr3_178952085-178952085_A_G Substitution Yes Yes  0.14%  7290 103 162 173 172 Cancer derived CGPLBR76 Breast II Germline KDR 1290S>N chr4_55946310-55946310_C_T Substitution NA Yes 36.57%  4342 104 166 171 179 Cancer CGPLBR76 Breast II Tumor- PIK3CA 10471-I>R chr3_178952085-178952085_A_G Substitution Yes Yes  0.12% 11785 100 165 168 177 Cancer derived CGPLBR77 Breast III Tumor- PTEN 170S>I chr10_89711891-89711891_G_T Substitution No Yes  2.29%  6161 100 158 166 169 Cancer derived CGPLBR80 Breast II Tumor- CDKN2A 12S, chr9_21974792-21974792_G_A Substitution No No  0.54%  3643  96 166 166 185 Cancer derived CGPLBR83 Breast II Germline AR 728N>O chrX_66937328-66937328_A_G Substitution NA Yes 42.66%  3479 106 162 164 174 Cancer CGPLBR83 Breast II Tumor- ATM 322E>K chr11_108117753-108117753_ Substitution No No  0.28%  3496 103 166 170 177 Cancer derived G_A CGPLBR83 Breast II Germline ERBB4 539Y>S chr2_212543783-212543783_T_G Substitution NA Yes 44.91%  1748 100 164 173 175 Cancer CGPLBR86 Breast II Germline STK11 354F, chr19_1223125-1223125_C_G Substitution NA Yes 42.32%  4241  98 160 168 175 Cancer CGPLBR86 Breast Germline SMARCB1 795+3A>G chr22_24159126-24159126_A_G Substitution NA Yes 43.38%  3096  88 160 167 174 Cancer CGPLBR87 Breast II Tumor- JAK2 215R>X chr9_5054591-5054591_C_T Substitution No No  0.35%  3680 101 162 168 175 Cancer derived CGPLBR87 Breast II Hemato- DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution Yes No  0.31%  6180 101 163 164 175 Cancer poietic CGPLBR87 Breast Tumor- SMAD4 496R>C chr18_48604664-48604664_C_T Substitution No No OAO%  7746  86 160 167 175 Cancer derived CGPLBR87 Breast Germline AR 6515<N chrX_66931310-66931310_G_A Substitution NA Yes 42.94%  2266 106 160 166 172 Cancer CGPLBR88 Breast Tumor- CDK6 51E>K chr7_92462487-92462487_C_T Substitution No No  0.13% 17537  89 185 200 223 Cancer derived CGPLBR88 Breast Germline APC 1125V>A chr5_112174665-112174665_T_C Substitution NA Yes 31.19%  5919 101 162 172 173 Cancer CGPLBR92 Breast Tumor- TP53 257L>P chr17_7577511-7577511_A_G Substitution No Yes  0.20% 15530  77 150 164 162 Cancer derived CGPLBR96 Breast II Tumor- TP53 213R>X chr17.fa:7578212-7578212_G_A Substitution Yes No  0.10%  9893 100 159 164 171 Cancer derived CGPLBR96 Breast II Hemato- DNMT3A 531D>G chr2_25467484-25467484_T_C Substitution No Yes  5.81%  8620  95 162 167 173 Cancer poietic CGPLBR96 Breast Tumor- AR 13R>Q chrX_66765026-66765026_G_A Substitution No No  0.60%  8036  85 162 169 175 Cancer derived CGPLBR97 Breast Hemato- DNMT3A 882R>H chr2_25457242-25457242_C_T Substitution Yes Yes  0.11% 14856  93 160 168 170 Cancer poietic CGPLBR97 Breast Germline PDGFRA 401A>D chr4_55136880-55136880_C_A Substitution NA Yes 34.12%  5329 100 161 165 171 Cancer CGPLBR97 Breast Tumor- GNAS 201R>H chr20_57484421-57484421_G_A Substitution Yes Yes  0.13%  7010  97 156 169 170 Cancer derived CGPLLU Lung Tumor- TP53 2415>F chr17_7577559-7577559_G_A Substitution Yes Yes  1.95% 11371 100 156 165 167 144 Cancer derived CGPLLU Lung Tumor- KRAS 12G>C chr12_25398285-25393285_C_A Substitution Yes Yes  5.10%  7641 100 155 167 166 144 Cancer derived CGPLLU Lung Tumor- EGFR 373P>5 chr7_55224336-55224336_C_T Substitution No Yes  0.16%  9996 100 158 168 169 144 Cancer derived CGPLLU Lung Tumor- ATM 292P>L chr11_108115727-108115727_ Substitution No No  0.22%  4956 101 159 166 169 144 Cancer derived C_T CGPLLU Lung Tumor- PIK3CA 545E>K chr3_178936091-178936091_G_A Substitution Yes Yes  2.94%  6540 100 153 170 166 144 Cancer derived CGPLLU Lung Tumor- ERBB4 426R>K chr2_212568841-212568841_C_T Substitution No No  0.18%  7648 101 156 164 166 144 Cancer derived CGPLLU Lung Hemato- JAK2 617V>F chr9_5073770-5073770_G_T Substitution Yes No  0.25%  5920 100 155 164 168 146 Cancer poietic CGPLLU Lung Tumor- TP53 282R>P chr17_7577093-7577093_C_G Substitution No Yes  1.30%  9356 100 155 166 168 146 Cancer derived CGPLLU Lung Hemato- DNMT3A 737L>H chr2_25463283-25463283_A_T Substitution No Yes  0.84%  7284 101 158 165 170 146 Cancer poietic CGPLLU Lung Tumor- RBI ⁸⁸¹+²C chr13_48937095-48937095_T_C Substitution No Yes  0.87%  4183 103 160 166 170 146 Cancer derived CGPLLU Lung Tumor- ATM 581L>F chr11_108122699-108122699_ Substitution No No  0.20%  6778 100 157 166 168 146 Cancer derived A_T CGPLLU Lung II Tumor- TP53 248R>Q chr17_7577538-7577538_C_T Substitution Yes No  0.15%  4807 100 155 166 170 147 Cancer derived CGPLLU Lung II Tumor- TP53 201L>X chr17_7578247-7578247_A_T Substitution No Yes  0.55%  5282 100 156 167 171 147 Cancer derived CGPLLU Lung II Tumor- ALK 1537G>E chr2_29416343-29416343_C_T Substitution No Yes  0.94%  7122 100 158 174 173 147 Cancer derived CGPLLU Lung II Germline PDGFRA 200T>S chr4_55130065-55130065_C_G Substitution NA Yes 43.47%  2825 101 160 165 173 147 Cancer CGPLLU Lung Tumor- CDKN2A 12S, chr9_21974792-21974792_G_A Substitution No No  0.22%  9940  95 161 164 174 162 Cancer derived CGPLLU Lung Tumor- EGFR 858L>R chr7_55259515-55259515_T_G Substitution Yes Yes  0.22% 13855  87 160 174 173 162 Cancer derived CGPLLU Lung Tumor- BRAF 354R>Q chr7_140494187-140494187_C_T Substitution No No  0.14% 11251 100 153 167 166 162 Cancer derived CGPLLU Lung Tumor- CDKN2A 12S, chr9_21974792-21974792_G_A Substitution No No  0.21% 10805  85 159 165 173 163 Cancer derived CGPLLU Lung Hemato- DNMT3A 528Y>D chr2_25467494-25467494_A_C Substitution No Yes  0.15% 20185  83 158 166 170 163 Cancer poietic CGPLLU Lung Tumor- STK11 2165>Y chr19_1220629-1220629_C_A Substitution No Yes  1.23%  6795  91 156 161 169 164 Cancer derived CGPLLU Lung Germline STK11 354F, chr19_1223125-1223125_C_G Substitution NA Yes 42.52%  4561  92 157 164 169 164 Cancer CGPLLU Lung Tumor- GNA11 606-3C>T chr19_3118919-3118919_C_T Substitution No No  0.20%  8097 100 158 170 170 164 Cancer derived CGPLLU Lung Tumor- TP53 278P>S chr17_7577106-7577106_G_A Substitution Yes No  0.10%  9241 100 155 165 167 164 Cancer derived CGPLLU Lung Tumor- TP53 161A>S chr17_7578449-7578449_C_A Substitution No Yes  1.78% 10806 100 157 168 169 164 Cancer derived CGPLLU Lung II Tumor- TP53 160M>I chr17_7578450-7578450_C_A Substitution No Yes  1.86% 10919 100 157 168 169 164 Cancer derived CGPLLU Lung II Tumor- ERBB4 1299P>L chr2_212248371-212248371_G_A Substitution No Yes  0.96%  5412 103 159 175 171 164 Cancer derived CGPLLU Lung Tumor- ERBB4 253N>S chr2_212587243-212587243_T_C Substitution No No  0.22%  5151 101 160 166 169 164 Cancer derived CGPLLU Lung Germline STK11 354F, chr19_1223125-1223125_C_G Substitution NA Yes 36.62%  7448  95 155 167 167 165 Cancer CGPLLU Lung Tumor- GNAS 201R>H chr20_57484421-57484421_G_A Substitution Yes Yes  0.16%  5822 102 154 166 166 165 Cancer derived CGPLLU Lung Tumor- TP53 136Q>X chr17.fa:7578524-7578524_G_A Substitution Yes Yes  0.06% 15985  97 152 165 166 168 Cancer derived CGPLLU Lung Hemato- DNMT3A 736R>S chr2_25463287-25463287_G_T Substitution No No  0.39% 11070 100 156 165 168 168 Cancer poietic CGPLLU Lung Tumor- EGFR 858L>R chr7.fa:55259515-55259515_T_G Substitution Yes Yes  0.07% 11063  83 157 166 169 168 Cancer derived CGPLLU Lung Tumor- STK11 597+1G>T chr19-122050-122050_G_T Substitution No Yes  0.33%  5881  88 162 165 174 174 Cancer derived CGPLLU Lung Tumor- JAK2 160D>Y chr9_5050695-5050695_G_T Substitution No Yes OAO%  3696 100 162 167 172 174 Cancer derived CGPLLU Lung Tumor- KRAS 12G>C chr12_25398285-25398285_C_A Substitution Yes Yes  0.16%  4941 101 162 167 172 174 Cancer derived CGPLLU Lung Hemato- DNMT3A 891R>W chr2_25457216-25457216_G_A Substitution No Yes  0.29%  7527 100 163 168 173 174 Cancer poietic CGPLLU Lung Hemato- DNMT3A 7151>M chr2_25463537-25463537_G_C Substitution No Yes  0.26%  8353 101 162 168 173 174 Cancer poietic CGPLLU Lung Tumor- TP53 179H>R chr17_7578394-7578394_T_C Substitution Yes Yes  8.03% 10214 100 160 166 170 175 Cancer derived CGPLLU Lung Hemato- DNMT3A 2598-1G>A chr2_25457290-25457290_C_T Substitution No No  0.21%  9739 100 157 168 168 175 Cancer poietic CGPLLU Lung Hemato- DNMT3A 755F, chr2_25463230-25463230_A_G Substitution No Yes  0.15%  9509 100 157 165 168 175 Cancer poietic CGPLLU Lung Germline ATM 337R>C chr11_108117798-108117798_ Substitution NA Yes 43.84%  2710 101 157 165 167 175 Cancer C_T CGPLLU Lung Tumor- ERBB4 941Q>X chr2_212288925-212288925_G_A Substitution No Yes  3.64%  6565 100 158 166 168 175 Cancer derived CGPLLU Lung Hemato- DNMT3A 750P>S chr2_25463245-25463245_G_A Substitution No Yes  0.92%  6513 101 164 168 175 176 Cancer poietic CGPLLU Lung Hemato- DNMT3A 735Y>C chr2_25463289-25463289_T_C Substitution No Yes  0.21%  5962 100 164 174 175 176 Cancer poietic CGPLLU Lung Tumor- KRAS 12G>V chr12_25398284-25398284_C_A Substitution Yes Yes  2.49%  7044 102 160 165 170 177 Cancer derived CGPLLU Lung Hemato- DNMT3A 897V>G chr2_25457197-25457197_A_C Substitution No Yes  1.53%  9950  88 160 169 171 177 Cancer poietic CGPLLU Lung Hemato- DNMT3A 882R>C chr2_25457243-25457243_G_A Substitution Yes No  0.29% 11233 100 160 168 171 177 Cancer poietic CGPLLU Lung Hemato- DNMT3A 2173+1G>A chr2_25463508-25463508_C_T Substitution No No  0.13% 10966  75 160 169 172 177 Cancer poietic CGPLLU Lung Tumor- CDH1 251T>M chr16_68844164-68844164_C_T Substitution No No  0.29%  8378 100 162 176 172 178 Cancer derived CGPLLU Lung Tumor- PIK3CA 861Q>X chr3_178947145-178947145_C_T Substitution No No  0.17%  7235 101 159 167 170 178 Cancer derived CGPLLU Lung Hemato- DNMT3A 879N>D chr2_25457252-25457252_T_C Substitution No Yes  0.38%  8350 103 161 169 171 179 Cancer poietic CGPLLU Lung Germline APC 2611T>I chr5_112179123_112179123_C_T Substitution NA Yes 39.91%  2609 108 162 171 173 179 Cancer CGPLLU180 Lung I Tumor- STK11 237D>Y chr19_1220691-1220591_G_T Substitution No Yes  2.43%  6085  91 158 165 170 Cancer derived CGPLLU180 Lung I Tumor- TP53 293G>V chr17_7577060-7577060_C_A Substitution No Yes  2.07%  8680  92 158 164 169 Cancer derived CGPLLU180 Lung I Tumor- TP53 282R>P chr17_7577093-7577093_C_G Substitution No Yes  1.94%  7790  92 158 167 168 Cancer derived CGPLLU180 Lung I Tumor- TP53 177P>L chr17.fa:7578400-7578400_G_A Substitution Yes No  0.08%  9036 101 160 169 171 Cancer derived CGPLLU180 Lung I Tumor- RBI 565S>X chr13_48955578-48955578_C_G Substitution No Yes  1.01%  4679 100 157 169 168 Cancer derived CGPLLU197 Lung I Hemato- DNMT3A 882R>C chr2_25457243-25457243_G_A Substitution Yes No  0.16%  7196 102 162 166 172 Cancer poietic CGPLLU197 Lung I Hemato- DNMT3A 879N>D chr2_25457252-25457252_T_C Substitution No No  0.38%  7147 100 161 166 172 Cancer poietic CGPLLU198 Lung I Tumor- TP53 1621>N chr17_7578445-7578445_A_T Substitution No Yes  0.87%  9322  97 157 165 168 Cancer derived CGPLLU198 Lung I Tumor- EGFR 858L>R chr7_55259515-55259515_T_G Substitution Yes Yes  0.52%  8303 100 160 173 172 Cancer derived CGPLLU202 Lung I Tumor- EGFR 790T>M chr7.fa:55249071-55249071_C_T Substitution Yes Yes  0.05% 14197  90 151 165 166 Cancer derived CGPLLU202 Lung I Tumor- EGFR 868E>X chr7_55259544-55259544_G_T Substitution No No  0.13%  9279  51 150 168 167 Cancer derived GGPLLU204 Lung I Tumor- KIT 956R>Q chr4_55604659-55604659_G_A Substitution No No  0.26%  7185 100 157 165 168 Cancer derived CGPLLU205 Lung II Hemato- DNMT3A 736R>C chr2_25463287-25463287_G_A Substitution No Yes  0.70% 10739  96 156 165 166 Cancer poietic CGPLLU205 Lung II Hemato- DNMT3A 696Q>X chr2_25463596-25463596_G_A Substitution No Yes  3.47% 12065 100 154 165 165 Cancer poietic CGPLLU206 Lung III Tumor- TP53 672+1G>A chr17_7578176-7578176_C_T Substitution Yes Yes 26.13%  6746  94 148 165 164 Cancer derived CGPLLU206 Lung III Tumor- TP53 131N>S chr17_7578538-7578538_T_C Substitution No No  0.21% 11225 100 147 167 164 Cancer derived CGPLLU207 Lung II Tumor- TP53 376-1G>A chr17_7578555-7578555_C_T Substitution Yes Yes  0.32% 11224 100 159 165 170 Cancer derived CGPLLU207 Lung II Germline ALK 419F, chr2_29606625-29606625_A_G Substitution NA Yes 34.58%  4960 101 160 166 170 Cancer CGPLLU207 Lung II Tumor- EGFR 790T>M chr7.fa:55249071-55249071_C_T Substitution Yes No  0.09% 13216  85 161 165 172 Cancer derived CGPLLU208 Lung II Tumor- TP53 250P>L chr17_7577532-7577532_G_A Substitution Yes Yes  1.33% 9211 101 156 166 168 Cancer derived CGPLLU208 Lung II Germline EGFR 224R>H chr7_55220281-55220281_G_A Substitution NA Yes 36.34%  5253 100 159 164 170 Cancer CGPLLU208 Lung II Tumor- EGFR 858L>R chr7_55259515-55259515_T_G Substitution Yes Yes  0.86% 10233 100 160 170 171 Cancer derived CGPLLU208 Lung II Tumor- MYC 98R>W chr8_128750755-128750755_C_T Substitution No No  0.17% 11421 100 158 165 171 Cancer derived CGPLLU209 Lung II Germline STK11 354F, chr19_1223125-1223125_C_G Substitution NA Yes 26.84% 11695  96 153 166 169 Cancer CGPLLU209 Lung II Tumor- TP53 100Q>X chr17_7579389-7579389_G_A Substitution No Yes  9.97% 12771  94 155 163 168 Cancer derived CGPLLU209 Lung II Tumor- CDKN2A 88E>X chr9_21971096-21971096_C_A Substitution Yes Yes  9.13% 16557  92 157 169 170 Cancer derived CGPLLU209 Lung II Tumor- PDGFRA 921A>T chr4_55155052-55155052_G_A Substitution No Yes  9.82% 13057  97 158 167 171 Cancer derived CGPLLU209 Lung II Germline EGFR 567M>V chr7_55231493-55231493_A_G Substitution NA Yes 30.41%  8521 100 155 167 169 Cancer CGPLOV10 Ovarian I Tumor- TP53 342R>X chr17_7574003-7574003_G_A Substitution Yes Yes  3.14%  4421 101 161 165 172 cancer derived CGPLOV11 Ovarian IV Tumor- TP53 248R>Q chr17_7577538-7577538_C_T Substitution Yes Yes  0.87%  7987 100 157 164 169 cancer derived CGPLOV11 Ovarian IV Germline TP53 63A>V chr17_7579499-7579499_G_A Substitution NA Yes 37.77%  3782  97 160 166 171 cancer CGPLOV13 Ovarian IV Tumor- ALK 444W>C chr2_29551298-29551298_C_A Substitution No Yes  0.12% 12072  88 157 165 169 cancer derived CGPLOV13 Ovarian IV Germline PDGFRA 401A>D chr4_55136880-55136880_C_A Substitution NA Yes 37.98%  4107 103 159 166 169 cancer CGPLOV13 Ovarian IV Tumor- KIT 135R>H chr4_55564516-55564516_G_A Substitution No Yes  0.35%  8427 100 161 165 171 cancer derived CGPLOV14 Ovarian I Tumor- HNF1A 230E>K chr12_121431484-121431484_ Substitution No No  0.14% 11418  92 154 167 171 cancer derived G_A CGPLOV15 Ovarian III Tumor- TP53 278P>S chr17_7577106-7577106_G_A Substitution Yes Yes  3.54%  7689 102 157 164 169 cancer derived CGPLOV15 Ovarian III Tumor- EGFR 433H>D chr7_55225445-55225445_C-G Substitution No No  0.19%  7617 101 159 167 171 cancer derived CGPLOV17 Ovarian I Tumor- TP53 248R>Q chr17_7577538-7577538_C_T Substitution Yes Yes  0.32%  4463  96 156 168 169 cancer derived CGPLOV17 Ovarian I Germline PDGFRA 1071D>N chr4_55161380-55161380_G_A Substitution NA Yes 44.10%  2884 110 157 170 170 cancer CGPLOV18 Ovarian I Germline APC 1125V>A chr5_112174665-112174665_T_C Substitution NA Yes 40.81%  2945 101 159 164 169 cancer CGPLOV19 Ovarian II Germline FGFR3 403K>E chr4_1806188-1806188_A_G Substitution NA Yes 23.80%  9727  95 158 167 172 cancer CGPLOV19 Ovarian II Tumor- TP53 273R>H chr17_7577120-7577120_C_T Substitution Yes Yes 36.83%  4387 100 158 165 169 cancer derived CGPLOV19 Ovarian II Germline AR 1765>R chrX_66765516-66765516_C_A Substitution NA Yes 65.29%  2775  93 161 171 171 cancer CGPLOV19 Ovarian II Tumor- APC 1378Q>X chr5_112175423-112175423_C_T Substitution Yes Yes 46.35%  3818 102 156 170 170 cancer derived CGPLOV20 Ovarian II Tumor- TP53 1951>T chr17_7578265-7578265_A_G Substitution Yes Yes  0.21%  5404  94 159 165 170 cancer derived CGPLOV20 Ovarian II Germline EGFR 253K>R chr7_55221714-55221714_A_G Substitution NA Yes 44.05%  3744 102 158 166 169 cancer CGPLOV21 Ovarian IV Germline STK11 354F, chr19_1223125-1223125_C_G Substitution NA Yes  7.68% 21823  81 158 166 169 cancer CGPLOV21 Ovarian IV Tumor- TP53 275C>Y chr17_7577114-7577114_C_T Substitution No Yes  2.04% 18806 101 159 165 169 cancer derived CGPLOV21 Ovarian IV Tumor- ERBB4 6025>T chr2_212530114-212530114_C_G Substitution No No 14.36% 10801  89 160 166 169 cancer derived CGPLOV22 Ovarian III Tumor- TP53 193H>P chr17_7578271-7578271_T_G Substitution No Yes  0.49% 11952 100 155 165 167 cancer derived CGPLOV22 Ovarian III Tumor- CTNNB1 41T>A chr3_41266124-41266124_A_G Substitution Yes Yes  0.34% 12399  92 150 165 164 cancer derived 75th 25th 75th Adjusted P Value of Mean Percentile Maximum Minimum Percentile Mutant Fragments Median Mean Percentile Maximum Difference between Difference between Difference between cfDNA cfDNA cfDNA cfDNA cfDNA Mode cfDNA cfDNA cfDNA cfDNA cfDNA Median Mutant and Mean Mutant and Mutant and Fragment Fragment Fragment Disinct Fragment Fragment Fragment Size Fragment Fragment Fragment Fragment Wild-type cfDNA Wild-type cfDNA Wild-type cfDNA Size (bp) Size (bp) Size (bp) Coverage Size (bp) Size (bp) (bp) Size (bp) Size (bp) Size (bp) Size (bp) Fragment Sizes (bp) Fragment Sizes (bp) Fragment Sizes 179 188 400 19 100 142 233 165 180 230 305 −4.0 1.54 0.475 182 185 400 21 132 166 182 176 191 198 309 7.0 &33 0.250 180 183 400 5411 92 152 167 169 186 191 399 0.0 189 0.000 177 182 400 1903 100 148 166 166 177 183 383 −1.0 −0.25 0.874 184 185 400 1344 108 155 167 170 189 191 398 1.0 137 aoos 181 182 400 2108 100 153 166 168 185 187 386 1.0 180 0.025 176 180 400 1951 101 149 175 167 179 182 397 0.0 2.65 0.148 176 183 399 75 123 162 167 172 182 190 370 3.0 131 0.368 177 182 400 28 101 130 130 139 164 155 345 −29.5 −12.79 0.000 183 188 399 6863 100 160 168 173 186 189 400 2.0 113 0.002 188 186 400 34 77 154 171 170 177 192 335 −0.5 −11A6 0.571 175 179 396 9 138 147 176 171 177 176 290 4.0 1.22 0.475 184 185 400 21 115 145 155 159 176 175 368 −11.0 −7.99 0.052 179 185 397 30 137 149 181 162 182 181 369 −8.0 149 0.061 179 182 397 44 125 155 155 169 185 194 338 0.0 178 0.623 185 188 400 8167 101 160 166 171 184 187 400 −1.0 −1.27 0.212 187 188 400 3562 102 158 168 170 185 185 399 0.0 −2.62 0.114 184 187 399 15 93 137 127 174 173 193 261 3.0 −11.00 0.507 183 185 400 26 137 163 166 167 179 180 364 −3.0 −4.34 0.430 181 182 397 35 118 147 176 163 172 176 336 −6.0 −9.35 0.166 172 175 400 71 133 152 170 165 169 173 301 0.0 −3.57 0.668 169 174 400 55 130 153 165 164 166 168 325 0.0 −2.15 amo 189 187 399 17 149 155 326 170 221 301 387 −3.0 32.43 0.453 176 183 400 18 156 170 174 174 210 219 372 5.0 33.84 0.368 169 175 397 51 108 143 268 152 164 178 268 −12.0 −5.12 0.000 166 173 397 26 118 147 153 156 174 168 327 −9.5 &37 0.036 184 186 400 45 116 151 168 163 175 177 346 −8.0 −8.84 0.057 185 188 400 25 157 165 191 175 207 199 350 3.0 22.93 0.465 185 187 400 25 124 168 180 180 189 191 338 8.0 .06 0.154 167 175 394 86 121 155 169 166 168 175 309 2.0 0.46 0.445 167 173 397 45 124 143 197 162 166 168 377 −1.0 −0.91 0.482 170 175 398 108 126 147 162 162 164 174 302 −3.0 −6.74 0.064 190 189 400 23 131 148 145 166 189 205 333 −5.0 0.80 0.297 182 182 399 42 138 155 155 174 177 187 343 5.5 −4.51 0.171 189 187 399 25 126 153 176 176 188 229 305 7.0 −0.19 0.234 192 193 400 977 101 149 189 170 182 192 380 −1.0 −9.76 0.000 173 179 391 525 102 140 168 159 168 176 382 −7.0 −5.57 0.052 181 185 399 4010 100 158 166 170 181 185 398 0.0 0.37 0.770 178 184 399 625 100 140 167 162 172 181 380 −9.0 −6.68 aoos 175 179 398 37 111 143 142 166 172 186 321 −1.0 −2.38 0.572 181 186 396 3184 102 159 168 172 182 187 400 0.5 0.95 0.564 180 183 399 47 111 148 144 169 176 183 353 −1.0 −4.83 0.598 183 184 397 39 111 146 182 162 182 185 337 −7.0 −0A4 0.064 185 184 400 24 110 146 309 182 208 284 355 1.0 22.31 0.031 176 180 400 32 117 146 154 157 167 166 298 −11L −8.94 0.013 180 184 399 43 111 143 144 177 187 212 319 9.0 7.22 0.062 185 187 400 29 109 140 204 159 188 204 387 −12L 132 0.031 179 182 399 20 128 152 180 163 166 180 219 −6.5 −13.04 0.155 176 184 398 7515 101 160 170 171 177 185 400 1.0 1.08 0.166 182 182 399 31 85 146 137 166 167 176 316 −3.0 −14.62 0.469 181 182 395 428 100 135 138 149 158 166 340 −20L −23A7 0.000 175 180 397 352 97 136 132 147 149 159 326 −21L −26.04 0.000 165 172 397 15 131 137 132 144 163 171 323 −20L −1.73 0.000 170 173 398 25 107 138 159 161 175 190 299 −3.0 ,83 0.384 171 173 400 27 122 147 161 161 173 171 342 −3.0 2.54 0.354 189 189 400 91 112 165 168 173 196 192 379 1.0 E83 0.571 189 189 400 27 124 144 154 154 167 172 320 −19L −2239 0.000 178 184 399 24 105 143 132 159 183 190 367 −11L 4.67 0.054 188 189 399 8 122 143 122 161 168 195 241 −13L −19.21 0.100 194 192 400 17 144 163 173 173 213 261 372 −1.0 19.22 0.587 180 183 394 15 132 159 186 166 174 185 265 −3.0 −5Z2 0.461 183 185 399 233 131 162 167 172 190 187 394 2.0 7.27 0.137 186 186 398 27 136 155 183 163 170 178 262 −7.0 −16.03 0.131 192 195 399 23 137 144 175 152 190 212 327 −17L −1.78 0.018 182 184 399 29 131 157 177 171 183 179 319 1.0 0.74 0.564 166 172 396 1616 100 146 164 159 163 170 354 −3.5 −3.57 0.000 175 180 400 806 96 158 169 169 179 184 366 1.0 180 0.054 165 172 399 1410 102 140 149 154 164 170 398 −8.0 −0.35 0316 170 177 397 49 99 153 143 182 206 284 333 1E0 36.25 0.000 166 173 398 33 140 155 154 170 180 180 296 73 14.38 0.104 180 178 400 73 95 140 140 155 173 178 324 −9.0 −636 0.000 172 177 400 38 115 160 164 167 182 179 329 1.5 10.09 0.479 171 174 386 6 124 137 170 156 153 168 178 −7.5 −1838 0.411 180 183 400 70 124 151 151 164 182 183 385 −6.0 1.71 0.064 194 199 399 6586 96 162 168 175 193 196 399 03 −1.79 0.166 184 188 400 41 112 172 176 177 195 195 373 33 11.02 0.397 194 198 399 35 149 168 175 175 181 186 312 1.0 −13A0 0.587 182 184 399 20 166 180 185 191 205 219 357 21.0 23.48 am 183 186 397 5338 102 159 175 171 183 185 394 −1.0 0.03 0.984 202 203 393 178 101 150 168 171 198 240 357 −5.0 −4.34 0.571 195 195 397 1350 104 153 163 171 201 258 400 03 5.94 am 185 189 400 1257 100 153 168 170 189 202 392 1.0 ,37 0.064 185 189 396 30 117 163 164 172 175 179 372 33 −10.29 0.463 203 210 391 336 105 153 141 171 200 240 399 −4.0 −3.10 0.571 188 194 399 741 101 161 169 176 190 194 400 23 1.96 0.571 193 193 396 89 100 145 171 171 197 229 393 −2.0 142 0.479 172 179 396 12 129 143 143 153 163 166 275 −143 −8.99 0.084 186 188 387 3559 91 155 164 173 195 211 398 33 E92 am 177 183 392 873 102 149 163 164 177 181 400 −3.0 −0.39 atm 194 200 377 1909 100 158 167 176 202 242 398 53 7.98 0.061 202 259 400 27 122 157 164 179 199 231 350 23 −332 0.685 171 178 395 1818 103 147 169 162 173 180 396 −1.0 1.92 0.372 178 182 374 546 102 151 166 166 180 182 381 03 2.87 0.416 179 184 397 26 132 142 138 171 183 188 351 15 129 0.572 195 194 400 53 117 157 166 169 192 198 336 −3.0 −236 0.451 176 179 397 40 124 150 169 166 181 176 309 −1.0 4.53 0.539 188 191 390 38 107 153 180 174 185 210 326 03 −2.59 0.576 205 207 399 217 102 146 144 163 188 212 360 −123 −17.11 0.004 196 195 397 266 111 147 150 166 188 204 379 −8.0 −7.53 0.208 186 184 400 76 123 157 171 169 182 182 346 1.0 −3.64 0.479 179 186 400 9832 93 161 166 172 180 186 399 −1.0 1.04 0.155 191 190 400 277 104 162 160 176 201 200 384 33 195 0.061 191 189 400 65 123 165 166 172 198 192 371 1.0 7.08 0.560 187 189 400 31 136 163 171 167 201 199 387 −4.0 14.14 0.341 202 202 400 5286 102 166 168 181 201 203 400 23 −038 0.587 196 201 400 102 138 166 161 179 199 209 372 −1.5 2.90 0.679 181 182 397 30 138 158 189 185 191 191 311 1E0 9.25 0.000 181 181 400 64 113 158 163 167 179 176 318 03 −235 0379 176 179 398 27 121 163 200 171 187 190 392 53 1039 0.314 191 192 398 2943 100 165 176 176 187 192 398 03 −333 am 179 181 399 25 138 153 138 167 181 184 340 −1.0 2.00 0.571 171 177 399 60 110 136 147 147 161 159 327 −193 −9.77 0.000 172 179 399 26 139 147 180 176 176 184 344 93 152 0.015 186 184 398 35 121 149 360 161 197 195 360 −9.0 10.77 0.314 176 178 397 4000 103 155 166 167 176 178 397 03 0.65 am 176 178 385 2390 99 157 164 168 178 180 400 03 1.78 0.314 182 184 400 28 131 160 168 167 177 179 338 −2.0 −533 0.463 194 193 400 3545 100 161 169 173 194 192 399 03 0.40 0325 179 180 398 15 121 146 166 166 172 204 221 −2.0 −7.32 0.564 188 187 400 2587 103 158 162 169 189 186 399 −1.0 1.12 0.598 189 192 400 86 121 165 183 177 189 193 373 33 −0.01 0.293 178 184 399 3339 101 157 165 169 177 184 400 03 −1.73 0.598 179 187 391 3193 101 163 178 173 180 186 389 −1.0 0.22 0.839 183 186 398 13 111 153 153 161 171 179 323 −11.0 −1236 0.061 197 201 400 4140 102 166 169 179 197 200 400 03 −0.32 0339 191 194 400 16 130 143 143 157 173 173 325 −203 −18A0 0.000 183 183 400 209 125 154 175 170 196 233 357 1.0 12.55 0.025 211 230 400 41 158 176 197 186 215 220 374 1.0 172 am 193 193 400 3445 94 162 175 174 194 194 399 03 035 0.714 197 199 400 23 123 182 248 224 232 260 359 47.0 34.97 0.000 193 195 399 1787 100 163 163 176 192 194 400 1.0 −035 0.718 204 207 400 4100 100 159 164 173 200 202 400 −2.0 −335 0.062 196 195 400 3096 79 159 161 173 194 191 397 −1.0 −2.45 0.251 202 203 400 73 142 178 178 184 237 338 377 9.0 35.30 0.114 205 203 400 23 161 168 168 171 189 186 380 −4.0 −1638 0.435 195 196 400 170 125 158 173 173 188 190 400 −2.0 −6.17 0.293 195 192 400 2089 101 162 169 176 203 203 400 4.5 E80 a000 238 280 400 125 84 192 194 207 243 324 400 −16L E51 a 574 197 194 400 5715 108 163 164 174 200 196 400 1.0 2.87 0.065 172 173 398 109 78 148 149 158 166 173 302 −40 −5.94 a 190 196 191 399 35 119 161 172 171 191 180 390 OL −4.34 0.627 189 190 400 826 102 162 166 171 187 187 395 −2.0 −1.94 0.475 194 195 400 95 135 160 161 170 182 184 400 −5.0 −11.54 a 155 184 184 400 27 128 150 150 169 174 185 319 −1.0 −9.68 0.571 179 184 399 4771 103 161 168 171 179 183 400 OL 0.15 0.880 187 185 399 7 147 154 154 167 164 174 177 −3.0 −22.90 0.155 179 179 395 330 106 152 166 166 178 178 361 −1.0 −1.35 0.685 172 177 399 536 106 151 167 163 172 175 363 −3.0 −0.34 0.880 179 183 400 45 138 163 175 172 185 191 380 3.0 E52 am 182 182 397 16 138 146 146 155 162 170 224 −14L −19.82 0007 172 177 397 293 101 152 169 164 170 174 392 −2.0 −1.37 0.646 171 177 399 23 130 152 162 162 163 177 232 −4.0 −7Z2 0.252 180 183 399 54 104 161 154 176 195 206 383 7.5 14.58 0.064 184 184 400 154 96 149 157 163 176 185 347 −5.5 −7Z7 a 154 186 187 399 79 102 163 177 174 200 203 372 4.0 14.61 0.270 183 185 400 44 118 149 163 163 185 188 338 −7.0 1.98 a 039 182 184 400 35 136 164 204 181 194 203 369 110 11.80 a 039 192 191 400 13 138 164 169 169 198 173 333 −1.0 E05 am 199 205 400 50 128 155 161 171 216 301 360 OL 17.02 0.623 191 193 400 81 108 150 108 173 198 224 385 OL E48 0.624 190 191 389 2597 101 159 165 172 185 187 397 −1.0 −5.17 0.005 192 197 400 58 92 173 192 192 202 200 397 1E0 E79 0.007 183 189 400 74 90 147 142 167 176 182 391 −6.5 −6.78 am 175 178 400 37 144 163 185 172 192 186 375 6.0 17.15 0.005 194 202 400 61 93 164 181 181 197 211 370 8.0 134 a 189 184 186 400 66 104 158 194 174 189 194 379 3.5 4.60 0.270 191 190 396 101 126 155 176 176 194 213 331 7.0 2.50 am 188 185 394 4718 100 156 164 168 190 187 393 −1.0 2.54 a 113 186 186 399 30 134 161 175 175 190 208 339 5.0 4.07 a 302 180 180 397 34 139 163 168 170 178 175 349 3.0 −1.65 0.407 182 182 400 262 101 150 152 165 181 186 393 −4.0 −0.65 0.876 182 182 400 277 101 150 147 166 182 185 393 −3.0 −0.36 0.926 180 182 395 65 121 158 161 167 186 188 338 −4.0 6.15 0.234 177 182 400 16 144 172 179 179 187 180 376 10.0 E98 0.130 185 184 399 7186 100 154 67 166 183 181 396 −1.0 −1.73 a 154 181 179 394 21 108 164 164 173 196 200 357 7.0 14.95 0.213 177 180 400 18 111 127 127 158 189 186 352 −8.0 12.47 0.179 179 181 400 72 121 156 173 166 183 179 396 −2.0 4.31 0.427 177 182 400 30 106 160 174 174 180 186 282 5.0 109 0.252 200 199 399 36 131 147 143 177 196 227 298 2.5 −4.24 0.479 184 185 392 20 144 173 266 178 199 215 269 6.0 15.13 0.252 182 184 395 16 147 156 156 164 177 169 302 −8.0 −4.82 0.119 186 187 399 34 159 168 168 176 206 196 365 3.0 20.55 0.415 186 186 396 5 116 182 182 185 201 192 329 12.0 14.62 0.263 185 183 399 1073 100 142 164 152 157 164 346 −18L −27.67 a000 179 180 400 46 109 151 143 175 174 183 325 7.0 −5.22 a 054 181 181 400 30 146 154 146 168 186 181 367 −0.5 5.19 0.568 176 179 392 2742 102 154 164 166 176 178 387 −1.0 −0.24 0.874 174 180 399 298 103 140 148 150 152 162 288 −18L −22.25 a000 197 194 399 67 115 164 250 173 187 201 366 −2.0 −9Z9 0.425 195 194 399 19 156 165 165 185 197 199 361 10.0 2.20 a 154 178 182 395 189 105 138 141 150 164 175 348 −20L −14.58 a000 183 185 398 227 123 160 168 169 185 184 396 −2.0 1.68 0.706 185 184 397 53 78 161 175 175 189 188 392 4.0 180 0.241 190 188 395 50 130 161 168 168 184 175 377 −4.5 −5.86 0.234 186 187 398 28 139 150 173 170 170 173 354 −2.5 −15.88 0.416 179 184 400 24 130 153 176 170 193 199 359 OL 13.13 0.598 185 185 394 48 111 154 170 168 173 183 295 −3.0 −11.80 0.270 189 187 398 2337 100 163 166 172 187 185 394 −1.0 −1.27 a 584 198 200 398 172 83 152 160 166 193 226 396 −4.0 −4.93 0.490 190 188 400 215 123 151 159 163 188 196 365 −6.0 −1.72 0.735 184 184 400 207 121 151 157 161 181 179 365 −7.0 −3.01 0.571 191 189 397 17 143 170 217 214 198 217 294 410 7.08 am 181 182 398 52 122 152 167 164 179 173 372 −4.5 −2.07 0.137 191 189 399 17 109 161 173 171 181 174 293 −1.0 −9.24 0.576 191 189 399 40 136 164 166 171 185 185 335 −1.0 −5.86 0.571 180 181 399 127 88 149 131 162 168 178 311 −6.0 −11.80 0.005 181 186 400 68 141 166 175 176 198 207 387 4.0 17.11 0.184 169 179 398 10 81 167 167 167 159 176 182 1.0 −10.20 0.589 170 181 398 33 107 162 167 167 174 185 322 0.0 4.57 0.636 175 181 391 23 112 156 190 164 175 190 349 −4.0 −0.92 0.308 175 177 400 109 130 153 169 166 175 178 382 0.0 −0.09 0.987 172 176 400 684 105 153 167 166 172 175 385 1.0 aoo 0.999 179 178 398 2946 100 138 157 155 172 174 398 −9.0 −7.28 am 175 178 399 30 121 165 165 176 198 219 325 12.0 22.37 am 187 186 400 63 140 155 154 167 201 215 372 −3.0 13.70 0.286 181 184 400 4754 101 160 170 170 179 181 393 0.0 −1.72 0.154 182 187 400 31 131 162 162 174 180 185 352 2.0 −2.26 0.494 181 183 400 150 110 144 166 162 176 173 385 −6.0 −5.86 0.314 179 184 400 5290 95 159 167 169 179 184 400 −1.0 0.11 0.909 181 186 400 140 101 155 175 167 179 180 352 −4.5 −2.77 0.589 187 190 397 20 92 141 241 168 178 209 283 −3.0 −9.82 0.479 190 192 400 8065 85 156 164 169 190 190 399 0.0 −0.08 0.942 174 182 400 2586 101 147 165 165 169 179 386 −3.5 −4.59 0.000 185 188 400 2808 100 150 158 167 189 200 399 −3.0 4.17 0.007 182 187 400 2227 100 154 162 171 183 190 398 0.0 1.00 0.564 176 183 396 8425 100 155 165 169 176 184 400 0.0 0.54 0.568 186 188 399 142 112 146 140 159 180 193 352 −13.0 −5A1 0.463 186 185 399 104 132 158 159 167 189 180 331 −2.0 105 0.657 183 185 392 3462 101 160 173 172 184 187 396 1.0 0.82 0.576 182 183 399 25 94 140 140 158 159 163 341 −11.0 −23A7 0.027 177 181 399 3789 101 159 168 169 176 181 395 0.0 −0.66 0.576 181 184 400 57 131 152 170 170 179 184 327 −1.0 −2A1 0.568 183 191 400 36 118 154 201 182 187 201 328 11.0 160 0.114 187 185 399 362 110 152 143 180 207 268 389 11.0 20.70 0.000 182 188 400 20 158 163 311 174 198 209 311 3.0 15.25 0.475 186 187 397 23 126 151 184 168 185 185 328 −1.0 −1A9 0.571 188 189 400 2980 100 158 169 170 187 189 398 0.0 −0.84 0.637 183 183 391 2793 91 158 167 170 181 182 389 1.0 −2.30 0.171 185 189 395 7357 100 158 175 171 182 187 399 −1.0 −2.37 0.008 184 184 398 5186 101 157 165 170 185 186 400 1.0 1.72 0.240 182 187 400 15595 64 159 167 170 181 185 397 −1.0 −1.39 0.245 186 185 400 6749 101 158 167 170 185 187 400 0.0 −0.52 0.702 193 190 400 23 127 148 148 194 222 292 378 24.0 29.58 0.027 182 185 394 3901 101 160 167 171 182 185 398 2.0 0.32 0.821 179 180 400 4633 100 158 169 170 185 187 400 1.0 6.16 am 175 179 400 734 101 151 155 165 176 178 366 −4.0 am 0.823 175 180 394 4022 101 159 167 168 172 178 399 −1.0 −2.84 am 184 182 400 117 116 156 156 172 199 184 399 5.0 15.08 0.084 172 176 395 65 109 145 177 167 181 181 306 3.0 all 0.293

TABLE 4 APPENDIX 0: Summary of whole genome cfDNA analyses Analysis Read Total Bases High Quality Patient Timepoint type Patient Type Length Sequenced Bases Analyzed Coverage CGCRC291 Preoperative treatment na 

ve WGS Colorectal Cancer 100  7232125000  4695396600  1.86 CGCRC292 Preoperative treatment na 

ve WGS Colorectal Cancer 100  6794092800  4471065400  1.77 CGCRC293 Preoperative treatment na 

ve WGS Colorectal Cancer 100  8373899600  5686176000  2.26 CGCRC294 Preoperative treatment na 

ve WGS Colorectal Cancer 100  8081312000  5347045800  2.12 CGCRC296 Preoperative treatment na 

ve WGS Colorectal Cancer 100 10072029200  6770998200  2.69 CGCRC299 Preoperative treatment na 

ve WGS Colorectal Cancer 100 10971591600  7632723200  3.03 CGCRC300 Preoperative treatment na 

ve WGS Colorectal Cancer 100  9894332600  6699951000  2.66 CGCRC301 Preoperative treatment na 

ve WGS Colorectal Cancer 100  7857346200  5021002000  1.99 CGCRC302 Preoperative treatment na 

ve WGS Colorectal Cancer 100 11671913000  8335275800  3.31 CGCRC304 Preoperative treatment na 

ve WGS Colorectal Cancer 100 19011739200 12957614200  5.14 CGCRC305 Preoperative treatment na 

ve WGS Colorectal Cancer 100  7177341400  4809957200  1.91 CGCRC306 Preoperative treatment na 

ve WGS Colorectal Cancer 100  8302233200  5608043600  2.23 CGCRC307 Preoperative treatment na 

ve WGS Colorectal Cancer 100  8034729400  5342620000  2.12 CGCRC308 Preoperative treatment na 

ve WGS Colorectal Cancer 100  8670084800  5934037200  2.35 CGCRC311 Preoperative treatment na 

ve WGS Colorectal Cancer 100  6947634400  4704601800  1.87 CGCRC315 Preoperative treatment na 

ve WGS Colorectal Cancer 100  5205544000  3419565400  1.36 CGCRC316 Preoperative treatment na 

ve WGS Colorectal Cancer 100  6405388600  4447534800  1.76 CGCRC317 Preoperative treatment na 

ve WGS Colorectal Cancer 100  6060390400  4104616600  1.63 CGCRC318 Preoperative treatment na 

ve WGS Colorectal Cancer 100  6848768600  4439404800  1.76 CGCRC319 Preoperative treatment na 

ve WGS Colorectal Cancer 100 10545294400  7355181600  2.92 CGCRC320 Preoperative treatment na 

ve WGS Colorectal Cancer 100  5961999200  3945054000  1.57 CGCRC321 Preoperative treatment na 

ve WGS Colorectal Cancer 100  8248095400  5614355000  2.23 CGCRC333 Preoperative treatment na 

ve WGS Colorectal Cancer 100 10540267600  6915490600  2.74 CGCRC336 Preoperative treatment na 

ve WGS Colorectal Cancer 100 10675581800  7087691800  2.81 CGCRC336 Preoperative treatment na 

ve WGS Colorectal Cancer 100 13788172600  8970308600  3.56 CGCRC341 Preoperative treatment na 

ve WGS Colorectal Cancer 100 10753467600  7311539200  2.90 CGCRC342 Preoperative treatment na 

ve WGS Colorectal Cancer 100 11836966000  7552793200  3.00 CGH14 Human adult elutriated WGS Healthy 100 36525427600 24950300200  9.90 lymphocytes CGH15 Human adult elutriated WGS Healthy 100 29930855000 23754049400  9.43 lymphocytes CGLU316 Pre-treatment, Day −53 WGS Lung Cancer 100 10354123200  6896471400  2.74 CGLU316 Pre-treatment, Day −4 WGS Lung Cancer 100  7870039200  5254938800  2.09 CGLU316 Post-treatment, Day 18 WGS Lung Cancer 100  8155322000  5416262400  2.15 CGLU316 Post-treatment, Day 87 WGS Lung Cancer 100  9442310400  6087893400  2.42 CGLU344 Pre-treatment, Day −21 WGS Lung Cancer 100  8728318600  5769097200  2.29 CGLU344 Pre-treatment, Day 0 WGS Lung Cancer 100 11710249400  7826902600  3.11 CGLU344 Post-treatment, Day 0.1875 WGS Lung Cancer 100 11569683000  7654701600  3.04 CGLU344 Post-treatment, Day 59 WGS Lung Cancer 100 11042459200  6320138800  2.51 CGLU369 Pre-treatment, Day −2 WGS Lung Cancer 100  8630932800  5779595800  2.29 CGLU369 Post-treatment, Day 12 WGS Lung Cancer 100  9227709600  6136755200  2.44 CGLU369 Post-treatment, Day 68 WGS Lung Cancer 100  7995282600  5239077200  2.08 CGLU369 Post-treatment, Day 110 WGS Lung Cancer 100  8750541000  5626139000  2.23 CGLU373 Pre-treatment, Day −2 WGS Lung Cancer 100 11746059600  7547485800  3.00 CGLU373 Post-treatment, Day 0.125 WGS Lung Cancer 100 13801136800  9255579400  3.67 CGLU373 Post-treatment, Day 7 WGS Lung Cancer 100 11537896800  7654111200  3.04 CGLU373 Post-treatment, Day 47 WGS Luna Cancer 100  8046326400  5397702400  2.14 CGPLBR100 Preoperative treatment na 

ve WGS Breast Cancer 100  8440532400  5729474800  2.27 CGPLBR101 Preoperative treatment na 

ve WGS Breast Cancer 100  9786253600  6673495200  2.65 CGPLBR102 Preoperative treatment na 

ve WGS Breast Cancer 100  8664980400  5669781600  2.25 CGPLBR103 Preoperative treatment na 

ve WGS Breast Cancer 100  9846936200  6662883400  2.64 CGPLBR104 Preoperative treatment na 

ve WGS Breast Cancer 100  9443375400  6497061000  2.58 CGPLBR12 Preoperative treatment na 

ve WGS Breast Cancer 100  7017577800  4823327400  1.91 CGPLBR18 Preoperative treatment na 

ve WGS Breast Cancer 100 10309652800  7130386000  2.83 CGPLBR23 Preoperative treatment na 

ve WGS Breast Cancer 100  9034484800  6219625800  2.47 CGPLBR24 Preoperative treatment na 

ve WGS Breast Cancer 100  9891454200  6601857400  2.62 CGPLBR28 Preoperative treatment na 

ve WGS Breast Cancer 100  7997607200  5400803200  2.14 CGPLBR30 Preoperative treatment na 

ve WGS Breast Cancer 100  8502597200  5885822400  2.34 CGPLBR31 Preoperative treatment na 

ve WGS Breast Cancer 100 12660085600  8551995600  3.39 CGPLBR32 Preoperative treatment na 

ve WGS Breast Cancer 100  8773498600  5839034600  2.32 CGPLBR33 Preoperative treatment na 

ve WGS Breast Cancer 100 10931742800  6967030600  2.76 CGPLBR34 Preoperative treatment na 

ve WGS Breast Cancer 100 10861398600  7453225800  2.96 CGPLBR35 Preoperative treatment na 

ve WGS Breast Cancer 100  9180193600  6158440200  2.44 CGPLBR36 Preoperative treatment na 

ve WGS Breast Cancer 100  9159948400  6091817800  2.42 CGPLBR37 Preoperative treatment na 

ve WGS Breast Cancer 100 10307505800  6929530600  2.75 CGPLBR36 Preoperative treatment na 

ve WGS Breast Cancer 100  9983824000  6841725400  2.71 CGPLBR40 Preoperative treatment na 

ve WGS Breast Cancer 100 10148823800  7024345400  2.79 CGPLBR41 Preoperative treatment na 

ve WGS Breast Cancer 100 11168192000  7562945800  3.00 CGPLBR45 Preoperative treatment na 

ve WGS Breast Cancer 100  8793780600  6011109400  2.39 CGPLBR46 Preoperative treatment na 

ve WGS Breast Cancer 100  7228607600  4706130000  1.87 CGPLBR47 Preoperative treatment na 

ve WGS Breast Cancer 100  7906911400  5341655000  2.12 CGPLBR46 Preoperative treatment na 

ve WGS Breast Cancer 100  6992032000  4428636200  1.76 CGPLBR49 Preoperative treatment na 

ve WGS Breast Cancer 100  7311195000  4559460200  1.81 CGPLBR50 Preoperative treatment na 

ve WGS Breast Cancer 100 11107960600  7582776600  3.01 CGPLBR51 Preoperative treatment na 

ve WGS Breast Cancer 100  8393547400  5102069000  2.02 CGPLBR52 Preoperative treatment na 

ve WGS Breast Cancer 100  9491894800  6141729000  2.44 CGPLBR55 Preoperative treatment na 

ve WGS Breast Cancer 100  9380109800  6518855200  2.59 CGPLBR56 Preoperative treatment na 

ve WGS Breast Cancer 100 12191816800  8293011200  3.29 CGPLBR57 Preoperative treatment na 

ve WGS Breast Cancer 100  9847584400  6713638000  2.66 CGPLBR59 Preoperative treatment na 

ve WGS Breast Cancer 100  7476477000  5059878200  2.01 CGPLBR60 Preoperative treatment na 

ve WGS Breast Cancer 100  6531354600  4331253800  1.72 CGPLBR61 Preoperative treatment na 

ve WGS Breast Cancer 100  9311029200  6430920800  2.55 CGPLBR63 Preoperative treatment na 

ve WGS Breast Cancer 100  8971949000  6044009600  2.40 CGPLBR65 Preoperative treatment na 

ve WGS Breast Cancer 100  7197301400  4835015200  1.92 CGPLBR68 Preoperative treatment na 

ve WGS Breast Cancer 100 10003774000  6974918800  2.77 CGPLBR69 Preoperative treatment na 

ve WGS Breast Cancer 100 10080881800  6903459200  2.74 CGPLBR70 Preoperative treatment na 

ve WGS Breast Cancer 100  8824002800  6002533800  2.38 CGPLBR71 Preoperative treatment na 

ve WGS Breast Cancer 100 10164136800  6994668600  2.78 CGPLBR72 Preoperative treatment na 

ve WGS Breast Cancer 100 18416841400 12328783000  4.89 CGPLBR73 Preoperative treatment na 

ve WGS Breast Cancer 100 10281460200  7078613200  2.81 CGPLBR76 Preoperative treatment na 

ve WGS Breast Cancer 100 10105270400  6800705000  2.70 CGPLBR81 Preoperative treatment na 

ve WGS Breast Cancer 100  5087126000  3273367200  1.30 CGPLBR82 Preoperative treatment na 

ve WGS Breast Cancer 100 10576496600  7186662600  2.85 CGPLBR63 Preoperative treatment na 

ve WGS Breast Cancer 100  8977124400  5947525000  2.36 CGPLBR84 Preoperative treatment na 

ve WGS Breast Cancer 100  6272538600  4066870600  1.61 CGPLBR87 Preoperative treatment na 

ve WGS Breast Cancer 100  8460954800  5375710200  2.13 CGPLBR88 Preoperative treatment na 

ve WGS Breast Cancer 100  8665810400  5499898200  2.18 CGPLBR90 Preoperative treatment na 

ve WGS Breast Cancer 100  6663469200  4392442400  1.74 CGPLBR91 Preoperative treatment na 

ve WGS Breast Cancer 100 10933002400  7647842000  3.03 CGPLBR92 Preoperative treatment na 

ve WGS Breast Cancer 100 10392674000  6493598000  2.58 CGPLBR93 Preoperative treatment na 

ve WGS Breast Cancer 100  5659836000  3931106800  1.56 CGPLH189 Preoperative treatment na 

ve WGS Healthy 100 11400610400  7655568800  3.04 CGPLH190 Preoperative treatment na 

ve WGS Healthy 100 11444671600  7581175203  3.01 CGPLH192 Preoperative treatment na 

ve WGS Healthy 100 12199010800  8126804800  3.22 CGPLH193 Preoperative treatment na 

ve WGS Healthy 100 10201897600  6635285400  2.63 CGPLH194 Preoperative treatment na 

ve WGS Healthy 100 11005087400  7081652600  2.81 CGPLH196 Preoperative treatment na 

ve WGS Healthy 100 12891462800  8646881800  3.43 CGPLH197 Preoperative treatment na 

ve WGS Healthy 100 11961841600  8052855200  3.20 CGPLH198 Preoperative treatment na 

ve WGS Healthy 100 13605489000  8885716000  3.53 CGPLH199 Preoperative treatment na 

ve WGS Healthy 100  1818090200  5615316000  2.23 CGPLH200 Preoperative treatment na 

ve WGS Healthy 100 14400027600  9310342000  3.69 CGPLH2O1 Preoperative treatment na 

ve WGS Healthy 100  6208766800  4171848400  1.66 CGPLH2O2 Preoperative treatment na 

ve WGS Healthy 100 11282922800  7363530600  2.92 CGPLH2O3 Preoperative treatment na 

ve WGS Healthy 100 13540689600  9068747600  3.60 CGPLH2O5 Preoperative treatment na 

ve WGS Healthy 100 10343537800  6696988600  2.66 CGPLH2O8 Preoperative treatment na 

ve WGS Healthy 100 12796300000  8272073400  3.28 CGPLH2O9 Preoperative treatment na 

ve WGS Healthy 100 13123035400  8531813600  3.39 CGPLH210 Preoperative treatment na 

ve WGS Healthy 100 10184218800  6832204600  2.71 CGPLH211 Preoperative treatment na 

ve WGS Healthy 100 14655260200  8887067600  3.53 CGPLH300 Preoperative treatment na 

ve WGS Healthy 100  7062083400  4553351200  1.81 CGPLH307 Preoperative treatment na 

ve WGS Healthy 100  7239128200  4547697200  1.80 CGPLH308 Preoperative treatment na 

ve WGS Healthy 100  8512551400  5526653600  2.19 CGPLH309 Preoperative treatment na 

ve WGS Healthy 100 11664474200  7431836600  2.95 CGPLH310 Preoperative treatment na 

ve WGS Healthy 100 11045691000  7451506200  2.96 CGPLH311 Preoperative treatment na 

ve WGS Healthy 100 10406803200  6786479600  2.69 CGPLH314 Preoperative treatment na 

ve WGS Healthy 100 10371343800  6925866600  2.75 CGPLH315 Preoperative treatment na 

ve WGS Healthy 100  9508538400  6208744600  2.46 CGPLH316 Preoperative treatment na 

ve WGS Healthy 100 10131063600  6891181000  2.73 CGPLH317 Preoperative treatment na 

ve WGS Healthy 100  8364314400  5302232600  2.10 CGPLH319 Preoperative treatment na 

ve WGS Healthy 100  8780528200  5585897000  2.22 CGPLH320 Preoperative treatment na 

ve WGS Healthy 100  8956232600  5784619200  2.30 CGPLH322 Preoperative treatment na 

ve WGS Healthy 100  9563837800  6445517800  2.56 CGPLH324 Preoperative treatment na 

ve WGS Healthy 100  6765038600  4469201600  1.77 CGPLH325 Preoperative treatment na 

ve WGS Healthy 100  8008213400  5099262803  2.02 CGPLH326 Preoperative treatment na 

ve WGS Healthy 100  9554226200  6112544800  2.43 CGPLH327 Preoperative treatment na 

ve WGS Healthy 100  8239168800  5351280200  2.12 CGPLH326 Preoperative treatment na 

ve WGS Healthy 100  7197086800  4516894800  1.79 CGPLH329 Preoperative treatment na 

ve WGS Healthy 100  8921554800  5493709800 2.18 CGPLH330 Preoperative treatment na 

ve WGS Healthy 100 10693603400  7077793600  2.81 CGPLH331 Preoperative treatment na 

ve WGS Healthy 100  8982792000  5538096200  2.20 CGPLH333 Preoperative treatment na 

ve WGS Healthy 100  7856985400  5178829600  2.06 CGPLH335 Preoperative treatment na 

ve WGS Healthy 100  9370663400  6035739400  2.40 CGPLH336 Preoperative treatment na 

ve WGS Healthy 100  8002498200  5340331400  2.12 CGPLH337 Preoperative treatment na 

ve WGS Healthy 100  7399022000  4954467600  1.97 CGPLH336 Preoperative treatment na 

ve WGS Healthy 100  8917121600  9170927200  2.45 CGPLH339 Preoperative treatment na 

ve WGS Healthy 100  8591130800  5866411400  2.33 CGPLH340 Preoperative treatment na 

ve WGS Healthy 100  8046351000  5368062000  2.13 CGPLH341 Preoperative treatment na 

ve WGS Healthy 100  7914788600  5200304800  2.06 CGPLH342 Preoperative treatment na 

ve WGS Healthy 100  8633473000  5701972400  2.26 CGPLH343 Preoperative treatment na 

ve WGS Healthy 100  6694769800  4410670800  1.75 CGPLH344 Preoperative treatment na 

ve WGS Healthy 100  7628192400  4961476600  1.97 CGPLH345 Preoperative treatment na 

ve WGS Healthy 100  7121569400  4747223000  1.88 CGPLH346 Preoperative treatment na 

ve WGS Healthy 100  7707924600  4873321600  1.93 CGPLH35 Preoperative treatment na 

ve WGS Healthy 100 47305985200  4774186200 12.63 CGPLH350 Preoperative treatment na 

ve WGS Healthy 100  9745839800  6054055200  2.40 CGPLH351 Preoperative treatment na 

ve WGS Healthy 100 13317435800  8714465000  3.46 CGPLH352 Preoperative treatment na 

ve WGS Healthy 100  7659351600  4752309400  1.89 CGPLH353 Preoperative treatment na 

ve WGS Healthy 100  8435782400  5275098200  2.09 CGPLH354 Preoperative treatment na 

ve WGS Healthy 100  8018644000  4857577600  1.93 CGPLH355 Preoperative treatment na 

ve WGS Healthy 100  8624675800  5709726400  2.27 CGPLH356 Preoperative treatment na 

ve WGS Healthy 100  8817952800  5729595200  2.27 CGPLH357 Preoperative treatment na 

ve WGS Healthy 100 11931696200  7690004400  3.05 CGPLH358 Preoperative treatment na 

ve WGS Healthy 100 12802561200  8451274800  3.35 CGPLH36 Preoperative treatment na 

ve WGS Healthy 100 40173545600  3974810400 10.52 CGPLH360 Preoperative treatment na 

ve WGS Healthy 100  7280078400  4918566200  1.95 CGPLH361 Preoperative treatment na 

ve WGS Healthy 100  7493498400  4966813800  1.97 CGPLH362 Preoperative treatment na 

ve WGS Healthy 100 11345644200  7532133600  2.99 CGPLH363 Preoperative treatment na 

ve WGS Healthy 100  6117382800  3965952400  1.57 CGPLH364 Preoperative treatment na 

ve WGS Healthy 100 10823498400  7195657000  2.86 CGPLH365 Preoperative treatment na 

ve WGS Healthy 100  5938367400  3954556200  1.57 CGPLH366 Preoperative treatment na 

ve WGS Healthy 100  7063168600  4731853000  1.88 CGPLH367 Preoperative treatment na 

ve WGS Healthy 100  7119631800  4627888200  1.84 CGPLH366 Preoperative treatment na 

ve WGS Healthy 100  7726718400  4975233400  1.97 CGPLH369 Preoperative treatment na 

ve WGS Healthy 100 10967584200  7130956800  2.83 CGPLH37 Preoperative treatment na 

ve WGS Healthy 100 45970545400  4591328800 12.15 CGPLH370 Preoperative treatment na 

ve WGS Healthy 100  9237170600  6106373800  2.42 CGPLH371 Preoperative treatment na 

ve WGS Healthy 100  8077798800  5237070600  2.08 CGPLH380 Preoperative treatment na 

ve WGS Healthy 100 14049589200  8614241200  3.42 CGPLH381 Preoperative treatment na 

ve WGS Healthy 100 16743792000 10767882800  4.27 CGPLH362 Preoperative treatment na 

ve WGS Healthy 100 18474025200 12276437200  4.87 CGPLH363 Preoperative treatment na 

ve WGS Healthy 100 13215954000  8430420600  3.35 CGPLH364 Preoperative treatment na 

ve WGS Healthy 100  8481814000  5463636200  2.17 CGPLH385 Preoperative treatment na 

ve WGS Healthy 100  9596118800  6445445600  2.56 CGPLH366 Preoperative treatment na 

ve WGS Healthy 100  7399540400  4915484800  1.95 CGPLH367 Preoperative treatment na 

ve WGS Healthy 100  6860332600  4339724400  1.72 CGPLH366 Preoperative treatment na 

ve WGS Healthy 100  8679705600  5463945400  2.17 CGPLH369 Preoperative treatment na 

ve WGS Healthy 100  7266863600  4702386000  1.87 CGPLH390 Preoperative treatment na 

ve WGS Healthy 100  7509035600  4913901800  1.95 CGPLH391 Preoperative treatment na 

ve WGS Healthy 100  7252286000  4702404800  1.87 CGPLH392 Preoperative treatment na 

ve WGS Healthy 100  7302618200  4722407000  1.87 CGPLH393 Preoperative treatment na 

ve WGS Healthy 100  8879138000  5947871800  2.36 CGPLH394 Preoperative treatment na 

ve WGS Healthy 100  8737031000  5599777400  2.22 CGPLH395 Preoperative treatment na 

ve WGS Healthy 100  7783904800  4907146000  1.95 CGPLH396 Preoperative treatment na 

ve WGS Healthy 100  7585567200  5076638200  2.01 CGPLH396 Preoperative treatment na 

ve WGS Healthy 100 13001418200  8607025000  3.42 CGPLH399 Preoperative treatment na 

ve WGS Healthy 100  9867699200  5526646000  2.19 CGPLH400 Preoperative treatment na 

ve WGS Healthy 100 10573939000  6290438200  2.50 CGPLH401 Preoperative treatment na 

ve WGS Healthy 100  9415150000  6139638000  2.44 CGPLH402 Preoperative treatment na 

ve WGS Healthy 100  5541458000  2972027800  1.18 CGPLH403 Preoperative treatment na 

ve WGS Healthy 100  6470913200  3549772600  1.41 CGPLH404 Preoperative treatment na 

ve WGS Healthy 100  7369651800  4120205000  1.64 CGPLH405 Preoperative treatment na 

ve WGS Healthy 100  7360239000  4293522600  1.70 CGPLH406 Preoperative treatment na 

ve WGS Healthy 100  6026125400  3426007400  1.36 CGPLH407 Preoperative treatment na 

ve WGS Healthy 100  7073375200  4079286800  1.62 CGPLH408 Preoperative treatment na 

ve WGS Healthy 100  8006103200  5121285600  2.03 CGPLH409 Preoperative treatment na 

ve WGS Healthy 100  7343124600  4432335600  1.76 CGPLH410 Preoperative treatment na 

ve WGS Healthy 100  7551842000  4818779600  1.91 CGPLH411 Preoperative treatment na 

ve WGS Healthy 100  6119676400  3636478400  1.44 CGPLH412 Preoperative treatment na 

ve WGS Healthy 100  7960821200  4935752200  1.96 CGPLH413 Preoperative treatment na 

ve WGS Healthy 100  7623405400  4827888400  1.92 CGPLH414 Preoperative treatment na 

ve WGS Healthy 100  7381312400  4743337200  1.88 CGPLH415 Preoperative treatment na 

ve WGS Healthy 100  7240754200  4162208800  1.65 CGPLH416 Preoperative treatment na 

ve WGS Healthy 100  7745658600  4570226000  1.85 CGPLH417 Preoperative treatment na 

ve WGS Healthy 100  7627498600  4403085600  1.75 CGPLH418 Preoperative treatment na 

ve WGS Healthy 100  9090285000  5094814000  2.02 CGPLH419 Preoperative treatment na 

ve WGS Healthy 100  7914120200  5078389800  2.02 CGPLH42 Preoperative treatment na 

ve WGS Healthy 100 39492040600  3901039400 10.32 CGPLH420 Preoperative treatment na 

ve WGS Healthy 100  7014307800  4711393600  1.87 CGPLH422 Preoperative treatment na 

ve WGS Healthy 100  9103972800  6053559800  2.40 CGPLH423 Preoperative treatment na 

ve WGS Healthy 100 10154714200  6128800200  2.43 CGPLH424 Preoperative treatment na 

ve WGS Healthy 100 11002394000  3573756000  2.61 CGPLH425 Preoperative treatment na 

ve WGS Healthy 100 14681352600  9272557000  3.68 CGPLH426 Preoperative treatment na 

ve WGS Healthy 100  8336731000  5177430800  2.05 CGPLH427 Preoperative treatment na 

ve WGS Healthy 100  8242924400  5632991800  2.24 CGPLH426 Preoperative treatment na 

ve WGS Healthy 100  8512550400  5604756600  2.22 CGPLH429 Preoperative treatment na 

ve WGS Healthy 100  8369802800  5477121400  2.17 CGPLH43 Preoperative treatment na 

ve WGS Healthy 100 38513193400  3815698400 10.10 CGPLH430 Preoperative treatment na 

ve WGS Healthy 100 10357365400  6841611000  2.71 CGPLH431 Preoperative treatment na 

ve WGS Healthy 100  7599875800  5006909000  1.99 CGPLH432 Preoperative treatment na 

ve WGS Healthy 100  7932532400  4932304200  1.96 CGPLH434 Preoperative treatment na 

ve WGS Healthy 100 10417028600  6965998800  2.76 CGPLH435 Preoperative treatment na 

ve WGS Healthy 100  8747793800  5677115200  2.25 CGPLH436 Preoperative treatment na 

ve WGS Healthy 100  7990589400  5228737800  2.07 CGPLH437 Preoperative treatment na 

ve WGS Healthy 100 10156991200  6935537200  2.75 CGPLH436 Preoperative treatment na 

ve WGS Healthy 100  9473604000  6445455600  2.56 CGPLH439 Preoperative treatment na 

ve WGS Healthy 100  8303723400  5439877200  2.16 CGPLH440 Preoperative treatment na 

ve WGS Healthy 100  9055233800  6018631400  2.39 CGPLH441 Preoperative treatment na 

ve WGS Healthy 100 10290682000  6896415200  2.74 CGPLH442 Preoperative treatment na 

ve WGS Healthy 100  9876551600  6591249800  2.62 CGPLH443 Preoperative treatment na 

ve WGS Healthy 100  9837225800  6360740800  2.52 CGPLH444 Preoperative treatment na 

ve WGS Healthy 100  9199271400  5755941600  2.28 CGPLH445 Preoperative treatment na 

ve WGS Healthy 100  8089236400  5218259800  2.07 CGPLH446 Preoperative treatment na 

ve WGS Healthy 100  7890664200  5181606000  2.06 CGPLH447 Preoperative treatment na 

ve WGS Healthy 100  7775775000  5120239800  2.03 CGPLH446 Preoperative treatment na 

ve WGS Healthy 100  8686964800  5605079200  2.22 CGPLH449 Preoperative treatment na 

ve WGS Healthy 100  8604545400  5527726600  2.19 CGPLH45 Preoperative treatment na 

ve WGS Healthy 100 39029653000  3771601200  9.98 CGPLH450 Preoperative treatment na 

ve WGS Healthy 100  8428254800  5439950000  2.16 CGPLH451 Preoperative treatment na 

ve WGS Healthy 100  8128977600  5186265600  2.06 CGPLH452 Preoperative treatment na 

ve WGS Healthy 100  6474313400  4216316400  1.67 CGPLH453 Preoperative treatment na 

ve WGS Healthy 100  9831832800  6224917600  2.47 CGPLH455 Preoperative treatment na 

ve WGS Healthy 100  7373753000  4593473600  1.82 CGPLH456 Preoperative treatment na 

ve WGS Healthy 100  8455416200  5457148200  2.17 CGPLH457 Preoperative treatment na 

ve WGS Healthy 100  8647618000  5534503800  2.20 CGPLH458 Preoperative treatment na 

ve WGS Healthy 100  6633156400  4415186000  1.75 CGPLH459 Preoperative treatment na 

ve WGS Healthy 100  8361048200  5497193800  2.18 CGPLH46 Preoperative treatment na 

ve WGS Healthy 100 35361484600  3516232800  9.30 CGPLH460 Preoperative treatment na 

ve WGS Healthy 100  6788835400  4472282800  1.77 CGPLH463 Preoperative treatment na 

ve WGS Healthy 100  8534880800  5481759200  2.18 CGPLH464 Preoperative treatment na 

ve WGS Healthy 100  6692520000  4184463400  1.66 CGPLH465 Preoperative treatment na 

ve WGS Healthy 100  7772884600  4878430800  1.94 CGPLH466 Preoperative treatment na 

ve WGS Healthy 100  9056275000  5830877400  2.31 CGPLH467 Preoperative treatment na 

ve WGS Healthy 100  6931419200  4585861000  1.82 CGPLH466 Preoperative treatment na 

ve WGS Healthy 100  9334067400  6314830400  2.51 CGPLH469 Preoperative treatment na 

ve WGS Healthy 100  7376691000  4545246600  1.80 CGPLH47 Preoperative treatment na 

ve WGS Healthy 100 38485647600  3534883600  9.35 CGPLH470 Preoperative treatment na 

ve WGS Healthy 100  7899727600  5221650600  2.07 CGPLH471 Preoperative treatment na 

ve WGS Healthy 100  9200430600  6102371000  2.42 CGPLH472 Preoperative treatment na 

ve WGS Healthy 100  8143742400  5399946600  2.14 CGPLH473 Preoperative treatment na 

ve WGS Healthy 100  8123924600  5419825400  2.15 CGPLH474 Preoperative treatment na 

ve WGS Healthy 100  8853071400  6084059400  2.41 CGPLH475 Preoperative treatment na 

ve WGS Healthy 100  8115374000  5291718000  2.10 CGPLH476 Preoperative treatment na 

ve WGS Healthy 100  8163162600  5096869600  2.02 CGPLH477 Preoperative treatment na 

ve WGS Healthy 100  8350093200  5465468600  2.17 CGPLH476 Preoperative treatment na 

ve WGS Healthy 100  8259642200  5406516200  2.15 CGPLH479 Preoperative treatment na 

ve WGS Healthy 100  8027598600  5417376800  2.15 CGPLH46 Preoperative treatment na 

ve WGS Healthy 100 42232410000  4165893400 11.02 CGPLH480 Preoperative treatment na 

ve WGS Healthy 100  7832983200  5020127000  1.99 CGPLH481 Preoperative treatment na 

ve WGS Healthy 100  7578518800  4883280800  1.94 CGPLH482 Preoperative treatment na 

ve WGS Healthy 100  8279364800  5652263600  2.24 CGPLH463 Preoperative treatment na 

ve WGS Healthy 100  8660338800  5823859200  2.31 CGPLH464 Preoperative treatment na 

ve WGS Healthy 100  8445420000  5794328000  2.30 CGPLH485 Preoperative treatment na 

ve WGS Healthy 100  8371255400  5490207800  2.18 CGPLH466 Preoperative treatment na 

ve WGS Healthy 100  8216712200  5506871000  2.19 CGPLH467 Preoperative treatment na 

ve WGS Healthy 100  7936294200  5309250200  2.11 CGPLH466 Preoperative treatment na 

ve WGS Healthy 100  8355603600  5453160000  2.16 CGPLH49 Preoperative treatment na 

ve WGS Healthy 100 33912191800  3310056000  8.76 CGPLH490 Preoperative treatment na 

ve WGS Healthy 100  7768712400  5175567800  2.05 CGPLH491 Preoperative treatment na 

ve WGS Healthy 100  9070904000  6011275000  2.39 CGPLH492 Preoperative treatment na 

ve WGS Healthy 100  7208727200  4753213800  1.89 CGPLH493 Preoperative treatment na 

ve WGS Healthy 100 10542882600  7225870800  2.87 CGPLH494 Preoperative treatment na 

ve WGS Healthy 100 10908197600  7046645000  2.80 CGPLH495 Preoperative treatment na 

ve WGS Healthy 100  8945040400  5891697800  2.34 CGPLH496 Preoperative treatment na 

ve WGS Healthy 100 10859729400  7549608000  3.00 CGPLH497 Preoperative treatment na 

ve WGS Healthy 100  9630507400  6473162800  2.57 CGPLH496 Preoperative treatment na 

ve WGS Healthy 100 10060232600  6744622800  2.68 CGPLH499 Preoperative treatment na 

ve WGS Healthy 100 10221293600  6951282800  2.76 CGPLH50 Preoperative treatment na 

ve WGS Healthy 100 41248860600  4073272800 10.78 CGPLH500 Preoperative treatment na 

ve WGS Healthy 100  9703168200  6239893800  2.48 CGPLH501 Preoperative treatment na 

ve WGS Healthy 100  9104779800  6161602800  2.45 CGPLH502 Preoperative treatment na 

ve WGS Healthy 100  8514467400  5290881400  2.10 CGPLH503 Preoperative treatment na 

ve WGS Healthy 100  9019992200  6100383400  2.42 CGPLH504 Preoperative treatment na 

ve WGS Healthy 100  9320330200  6199750200  2.46 CGPLH505 Preoperative treatment na 

ve WGS Healthy 100  7499497400  4914559000  1.95 CGPLH506 Preoperative treatment na 

ve WGS Healthy 100 10526142000  6963312600  2.76 CGPLH507 Preoperative treatment na 

ve WGS Healthy 100  9091018400  6146678600  2.44 CGPLH508 Preoperative treatment na 

ve WGS Healthy 100 10989315600  7360201400  2.92 CGPLH509 Preoperative treatment na 

ve WGS Healthy 100  9729084600  6702691600  2.66 CGPLH51 Preoperative treatment na 

ve WGS Healthy 100 35967451400  3492833200  9.24 CGPLH510 Preoperative treatment na 

ve WGS Healthy 100 11162691600  7626795400  3.03 CGPLH511 Preoperative treatment na 

ve WGS Healthy 100 11888619600  8110427600  3.22 CGPLH512 Preoperative treatment na 

ve WGS Healthy 100 10726438400  7110078000  2.82 CGPLH513 Preoperative treatment na 

ve WGS Healthy 100 10701564200  7155271400  2.84 CGPLH514 Preoperative treatment na 

ve WGS Healthy 100  8822067000  5958773800  2.36 CGPLH515 Preoperative treatment na 

ve WGS Healthy 100  7792074800  5317464600  2.11 CGPLH516 Preoperative treatment na 

ve WGS Healthy 100  8642620000  5846439400  2.32 CGPLH517 Preoperative treatment na 

ve WGS Healthy 100 11915929600  8013937000  3.18 CGPLH518 Preoperative treatment na 

ve WGS Healthy 100 12804517400  8606661600  3.42 CGPLH519 Preoperative treatment na 

ve WGS Healthy 100 11513222200  7922798400  3.14 CGPLH52 Preoperative treatment na 

ve WGS Healthy 100 49247304200  4849631400 12.83 CGPLH520 Preoperative treatment na 

ve WGS Healthy 100  8942102400  6030683400  2.39 CGPLH54 Preoperative treatment na 

ve WGS Healthy 100 45399346400  4466164600 11.82 CGPLH55 Preoperative treatment na 

ve WGS Healthy 100 42547725000  4283337600 11.33 CGPLH56 Preoperative treatment na 

ve WGS Healthy 100 33460308000  3226338000  8.53 CGPLH57 Preoperative treatment na 

ve WGS Healthy 100 36504735200  3509125000  9.28 CGPLH59 Preoperative treatment na 

ve WGS Healthy 100 39642810600  3820011000 10.11 CGPLH625 Preoperative treatment na 

ve WGS Healthy 100  6408225000  4115487600  1.63 CGPLH626 Preoperative treatment na 

ve WGS Healthy 100  9915193600  6391657000  2.54 CGPLH63 Preoperative treatment na 

ve WGS Healthy 100 37447047600  3506737000  9.28 CGPLH639 Preoperative treatment na 

ve WGS Healthy 100  8158965800  5216049600  2.07 CGPLH64 Preoperative treatment na 

ve WGS Healthy 100 34275506800  3264508000  8.63 CGPLH640 Preoperative treatment na 

ve WGS Healthy 100  8058876800  5333551800  2.12 CGPLH642 Preoperative treatment na 

ve WGS Healthy 100  7545555600  4909732800  1.95 CGPLH643 Preoperative treatment na 

ve WGS Healthy 100  7865776800  5254772000  2.09 CGPLH644 Preoperative treatment na 

ve WGS Healthy 100  6890139000  4599387400  1.83 CGPLH646 Preoperative treatment na 

ve WGS Healthy 100  7757219400  5077408200  2.01 CGPLH75 Preoperative treatment na 

ve WGS Healthy 100 23882926000  2250344400  5.95 CGPLH76 Preoperative treatment na 

ve WGS Healthy 100 30631483600  3086042200  8.16 CGPLH77 Preoperative treatment na 

ve WGS Healthy 100 31651741400  3041290200  8.04 CGPLH76 Preoperative treatment na 

ve WGS Healthy 100 31165831200  3130079800  8.28 CGPLH79 Preoperative treatment na 

ve WGS Healthy 100 31935043000  3128488200  8.27 CGPLH80 Preoperative treatment na 

ve WGS Healthy 100 32965093000  3311371800  8.76 CGPLH81 Preoperative treatment na 

ve WGS Healthy 100 27035311200  2455084400  6.49 CGPLH82 Preoperative treatment na 

ve WGS Healthy 100 28447051200  2893358200  7.65 CGPLH63 Preoperative treatment na 

ve WGS Healthy 100 26702240200  2459494000  6.50 CGPLH64 Preoperative treatment na 

ve WGS Healthy 100 25176861400  2524467400  6.68 CGPLLU13 Pre-treatment, Day −2 WGS Lung Cancer 100  9126585600  5915061800  2.35 CGPLLU13 Post-treatment, Day 5 WGS Lung Cancer 100  7739120200  5071745800  2.01 CGPLLU13 Post-treatment, Day 28 WGS Lung Cancer 100  9081585400  5764371600  2.29 CGPLLU13 Post-treatment, Day 91 WGS Lung Cancer 100  9576557000  6160760200  2.44 CGPLLU14 Pre-treatment, Day −36 WGS Lung Cancer 100 13659198400  9033455800  3.58 CGPLLU14 Pre-treatment, Day −16 WGS Lung Cancer 100  7178855800  4856648600  1.93 CGPLLU14 Pre-treatment, Day −3 WGS Lung Cancer 100  7653473000  4816193600  1.91 CGPLLU14 Pre-treatment, Day 0 WGS Lung Cancer 100  7851997400  5193256600  2.06 CGPLLU14 Post-treatment, Day 0.33 WGS Lung Cancer 100  7193040800  4869701600  1.93 CGPLLU14 Post-treatment, Day 7 WGS Lung Cancer 100  7102050000  4741432600  1.88 CGPLLU144 Preoperative treatment na 

ve WGS Lung Cancer 100  4934813600  3415936400  1.36 CGPLLU147 Preoperative treatment na 

ve WGS Lung Cancer 100 24409561000  2118672800  5.61 CGPLLU161 Preoperative treatment na 

ve WGS Lung Cancer 100  8998813400  6016145000  2.39 CGPLLU162 Preoperative treatment na 

ve WGS Lung Cancer 100  9709792400  6407866400  2.54 CGPLLU163 Preoperative treatment na 

ve WGS Lung Cancer 100  9150620200  6063569800  2.41 CGPLLU165 Preoperative treatment na 

ve WGS Lung Cancer 100 28374436400  2651138600  7.01 CGPLLU168 Preoperative treatment na 

ve WGS Lung Cancer 100  5692739400  3695191000  1.47 CGPLLU169 Preoperative treatment na 

ve WGS Lung Cancer 100  9093975600  5805320800  2.30 CGPLLU175 Preoperative treatment na 

ve WGS Lung Cancer 100 33794816800  3418750400  9.04 CGPLLU176 Preoperative treatment na 

ve WGS Lung Cancer 100  8778553800  5794950200  2.30 CGPLLU177 Preoperative treatment na 

ve WGS Lung Cancer 100  3734614800  2578696200  1.02 CGPLLU180 Preoperative treatment na 

ve WGS Lung Cancer 100 28305936600  2756034200  7.29 CGPLLU198 Preoperative treatment na 

ve WGS Lung Cancer 100 23244959200  2218577200  5.86 CGPLLU202 Preoperative treatment na 

ve WGS Lung Cancer 100 21110128200  1831279400  4.84 CGPLLU203 Preoperative treatment na 

ve WGS Lung Cancer 100  4304235600  2896429000  1.15 CGPLLU205 Preoperative treatment na 

ve WGS Lung Cancer 100 10502467000  7386984800  2.93 CGPLLU206 Preoperative treatment na 

ve WGS Lung Cancer 100 21888248200  2026666000  5.36 CGPLLU207 Preoperative treatment na 

ve WGS Lung Cancer 100 10806230600  7363049000  2.92 CGPLLU208 Preoperative treatment na 

ve WGS Lung Cancer 100  7795426800  5199545800  2.06 CGPLLU209 Preoperative treatment na 

ve WGS Lung Cancer 100 26174542000  2621961800  6.93 CGPLLU244 Pre-treatment, Day −7 WGS Lung Cancer 100  9967531400  6704365800  2.66 CGPLLU244 Pre-treatment, Day −1 WGS Lung Cancer 100  9547119200  5785172600  2.30 CGPLLU244 Post-treatment, Day 6 WGS Lung Cancer 100  9535898600  6452174000  2.56 CGPLLU244 Post-treatment, Day 62 WGS Lung Cancer 100  8783628600  5914149000  2.35 CGPLLU245 Pre-treatment, Day −32 WGS Lung Cancer 100 10025823200  6313303800  2.51 CGPLLU245 Pre-treatment, Day 0 WGS Lung Cancer 100  9462480400  6612867800  2.62 CGPLLU245 Post-treatment, Day 7 WGS Lung Cancer 100  9143825000  6431013200  2.55 CGPLLU245 Post-treatment, Day 21 WGS Lung Cancer 100  9072713800  6368533000  2.53 CGPLLU246 Pre-treatment, Day −21 WGS Lung Cancer 100  9579787000  6458003400  2.56 CGPLLU246 Pre-treatment, Day 0 WGS Lung Cancer 100  9512703600  6440535600  2.56 CGPLLU246 Post-treatment, Day 9 WGS Lung Cancer 100  9512646000  6300939200  2.50 CGPLLU246 Post-treatment, Day 42 WGS Lung Cancer 100 11136103000  7358747400  2.92 CGPLLU264 Pre-treatment, Day −1 WGS Lung Cancer 100  9196305000  6239803600  2.48 CGPLLU264 Post-treatment, Day 6 WGS Lung Cancer 100  8247416600  5600454200  2.22 CGPLLU264 Post-treatment, Day 27 WGS Lung Cancer 100  8681022200  5856109000  2.32 CGPLLU264 Post-treatment, Day 69 WGS Lung Cancer 100  8931976400  5974246000  2.37 CGPLLU265 Pre-treatment, Day 0 WGS Lung Cancer 100  9460534000  6111185200  2.43 CGPLLU265 Post-treatment, Day 3 WGS Lung Cancer 100  8051601200  4984166600  1.98 CGPLLU265 Post-treatment, Day 7 WGS Lung Cancer 100  8082224600  5110092600  2.03 CGPLLU265 Post-treatment, Day 84 WGS Lung Cancer 100  8368637400  5369526400  2.13 CGPLLU266 Pre-treatment, Day 0 WGS Lung Cancer 100  8583766400  5846473600  2.32 CGPLLU266 Post-treatment, Day 16 WGS Lung Cancer 100  8795793600  5984531400  2.37 CGPLLU266 Post-treatment, Day 83 WGS Lung Cancer 100  9157947600  6227735003  2.47 CGPLLU266 Post-treatment, Day 328 WGS Lung Cancer 100  7299455400  5049379000  2.00 CGPLLU267 Pre-treatment, Day −1 WGS Lung Cancer 100 10658657800  6892067000  2.73 CGPLLU267 Post-treatment, Day 34 WGS Lung Cancer 100  8492833400  5101097800  2.02 CGPLLU267 Post-treatment, Day 90 WGS Lung Cancer 100 12030314800  7757930400  3.08 CGPLLU269 Pre-treatment, Day 0 WGS Lung Cancer 100  9170168000  5830454400  2.31 CGPLLU269 Post-treatment, Day 9 WGS Lung Cancer 100  8905640400  5298461400  2.10 CGPLLU269 Post-treatment, Day 28 WGS Lung Cancer 100  8455306600  5387927400  2.14 CGPLLU271 Post-treatment, Day 259 WGS Lung Cancer 100  8112060400  5404979000  2.14 CGPLLU271 Pre-treatment, Day 0 WGS Lung Cancer 100 13150818200  8570453400  3.40 CGPLLU271 Post-treatment, Day 6 WGS Lung Cancer 100  9008880600  5854051400  2.32 CGPLLU271 Post-treatment, Day 20 WGS Lung Cancer 100  8670913000  5461577000  2.17 CGPLLU271 Post-treatment, Day 104 WGS Lung Cancer 100  8887441400  5609039000  2.23 CGPLLU43 Pre-treatment, Day −1 WGS Lung Cancer 100  8407811200  5203486400  2.06 CGPLLU43 Post-treatment, Day 6 WGS Lung Cancer 100  9264335200  5626714400  2.23 CGPLLU43 Post-treatment, Day 27 WGS Lung Cancer 100  8902283000  5485656200  2.18 CGPLLU43 Post-treatment, Day 83 WGS Lung Cancer 100  9201509200  5875084200  2.33 CGPLLU86 Pre-treatment, Day 0 WGS Lung Cancer 100  9152729200  6248173200  2.48 CGPLLU86 Post-treatment, Day 0.5 WGS Lung Cancer 100  6703253000  4663026800  1.85 CGPLLU86 Post-treatment, Day 7 WGS Lung Cancer 100  6590121400  4559562400  1.81 CGPLLU86 Post-treatment, Day 17 WGS Lung Cancer 100  8653551800  5900136000  2.34 CGPLLU88 Pre-treatment, Day 0 WGS Lung Cancer 100  8096528000  5505475400  2.18 CGPLLU88 Post-treatment, Day 7 WGS Lung Cancer 100  8283192200  5784217600  2.30 CGPLLU88 Post-treatment, Day 297 WGS Lung Cancer 100  9297110800  6407258000  2.54 CGPLLU89 Pre-treatment, Day 0 WGS Lung Cancer 100  7842145200  5356095400  2.13 CGPLLU89 Post-treatment, Day 7 WGS Lung Cancer 100  7234220200  4930375200  1.96 CGPLLU89 Post-treatment, Day 22 WGS Lung Cancer 100  6242889800  4057361000  1.61 CGPLOV11 Preoperative treatment na 

ve WGS Ovarian Cancer 100  8985130400  5871959600  2.33 CGPLOV12 Preoperative treatment na 

ve WGS Ovarian Cancer 100  9705820000  6430505400  2.55 CGPLOV13 Preoperative treatment na 

ve WGS Ovarian Cancer 100 10307949400  7029712000  2.79 CGPLOV15 Preoperative treatment na 

ve WGS Ovarian Cancer 100  8472829400  5562142400  2.21 CGPLOV16 Preoperative treatment na 

ve WGS Ovarian Cancer 100 10977781000  7538581600  2.99 CGPLOV19 Preoperative treatment na 

ve WGS Ovarian Cancer 100  8800876200  5855304000  2.32 CGPLOV20 Preoperative treatment na 

ve WGS Ovarian Cancer 100  8714443600  5695165800  2.26 CGPLOV21 Preoperative treatment na 

ve WGS Ovarian Cancer 100 10180394800  7120260400  2.83 CGPLOV22 Preoperative treatment na 

ve WGS Ovarian Cancer 100 10107760000  6821916800  2.71 CGPLOV23 Preoperative treatment na 

ve WGS Ovarian Cancer 100 10643399800  7206330800  2.86 CGPLOV24 Preoperative treatment na 

ve WGS Ovarian Cancer 100  6780929000  4623300400  1.83 CGPLOV25 Preoperative treatment na 

ve WGS Ovarian Cancer 100  7817548600  5359975200  2.13 CGPLOV26 Preoperative treatment na 

ve WGS Ovarian Cancer 100 11763101400  8178024400  3.25 CGPLOV26 Preoperative treatment na 

ve WGS Ovarian Cancer 100  9522546400  6259423400  2.48 CGPLOV31 Preoperative treatment na 

ve WGS Ovarian Cancer 100  9104831200  6109358400  2.42 CGPLOV32 Preoperative treatment na 

ve WGS Ovarian Cancer 100  9222073600  6035150000  2.39 CGPLOV37 Preoperative treatment na 

ve WGS Ovarian Cancer 100  8898328600  5971018200  2.37 CGPLOV38 Preoperative treatment na 

ve WGS Ovarian Cancer 100  8756825200  5861536600  2.33 CGPLOV40 Preoperative treatment na 

ve WGS Ovarian Cancer 100  9709391600  6654707200  2.64 CGPLOV41 Preoperative treatment na 

ve WGS Ovarian Cancer 100  8923625000  5973070400  2.37 CGPLOV42 Preoperative treatment na 

ve WGS Ovarian Cancer 100 10719380400  7353214200  2.92 CGPLOV43 Preoperative treatment na 

ve WGS Ovarian Cancer 100 10272189000  6423288600  2.55 CGPLOV44 Preoperative treatment na 

ve WGS Ovarian Cancer 100  9861862600  6769185800  2.69 CGPLOV46 Preoperative treatment na 

ve WGS Ovarian Cancer 100  8788956400  5789863400  2.30 CGPLOV47 Preoperative treatment na 

ve WGS Ovarian Cancer 100  9380561800  6480763600  2.57 CGPLOV48 Preoperative treatment na 

ve WGS Ovarian Cancer 100  9258552600  6380106400  2.53 CGPLOV49 Preoperative treatment na 

ve WGS Ovarian Cancer 100  8787025400  6134503600  2.43 CGPLOV50 Preoperative treatment na 

ve WGS Ovarian Cancer 100 10144154400  6984721400  2.77 CGPLPA112 Preoperative treatment na 

ve WGS Pancreatic Cancer 100 12740651400  9045622000  3.59 CGPLPA113 Preoperative treatment na 

ve WGS Duodenal Cancer 100  8802479000  5909030800  2.34 CGPLPA114 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  8792313600  6019061000  2.39 CGPLPA115 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  8636551400  5958809000  2.36 CGPLPA117 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  9128885200  6288833200  2.50 CGPLPA118 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  7931485800  5407532800  2.15 CGPLPA122 Preoperative treatment na 

ve WGS Bile Duct Cancer 100 10888985000  7530118800  2.99 CGPLPA124 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  8562012400  5860171000  2.33 CGPLPA125 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  9715576600  6390321000  2.54 CGPLPA126 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  8056768800  5651600800  2.24 CGPLPA127 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  8000301000  5382987600  2.14 CGPLPA128 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  6165751600  4256521400  1.69 CGPLPA129 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  7143147400  4917370400  1.95 CGPLPA130 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  5664335000  3603919400  1.43 CGPLPA131 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  8292982000  5844942000  2.32 CGPLPA134 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  7088917000  5048887600  2.00 CGPLPA135 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  8759665600  5800618200  2.30 CGPLPA136 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  7539715800  5248227600  2.08 CGPLPA137 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  8391815400  5901273800  2.34 CGPLPA139 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  8992280200  6328314400  2.51 CGPLPA14 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  8787706200  5731317600  2.27 CGPLPA140 Preoperative treatment na 

ve WGS Bile Duct Cancer 100 16365641800 11216732000  4.45 CGPLPA141 Preoperative treatment na 

ve WGS Bile Duct Cancer 100 15086298000 10114790200  4.01 CGPLPA15 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  8255566800  5531677600  2.20 CGPLPA155 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  9457155800  6621881800  2.63 CGPLPA156 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  9845385800  6728653000  2.67 CGPLPA165 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  8356604600  5829895800  2.31 CGPLPA168 Preoperative treatment na 

ve WGS Bile Duct Cancer 100 10365661600  7048115600  2.80 CGPLPA17 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  8073547400  4687808000  1.86 CGPLPA184 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  9014218400  6230922200  2.47 CGPLPA187 Preoperative treatment na 

ve WGS Bile Duct Cancer 100  8883536200  6140874400  2.44 CGPLPA23 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  9835452000  6246525400  2.48 CGPLPA25 Preoperative treatment na 

ve WGS Pancreatic Cancer 100 10077515400  6103322200  2.42 CGPLPA26 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  8354272400  5725781000  2.27 CGPLPA28 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  8477461600  5688846800  2.26 CGPLPA33 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  7287615600  4596723800  1.82 CGPLPA34 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  6122902400  4094828000  1.62 CGPLPA37 Preoperative treatment na 

ve WGS Pancreatic Cancer 100 12714888200  8527779200  3.38 CGPLPA38 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  8525500600  5501341400  2.18 CGPLPA39 Preoperative treatment na 

ve WGS Pancreatic Cancer 100 10502663600  6812333000  2.70 CGPLPA40 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  9083670000  5394717800  2.14 CGPLPA42 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  5972126600  3890395200  1.54 CGPLPA46 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  4720090200  2626298800  1.04 CGPLPA47 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  7317385800  4543833000  1.80 CGPLPA46 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  7553856200  5022695600  1.99 CGPLPA52 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  5655875000  3551861600  1.41 CGPLPA53 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  9504749000  6323344800  2.51 CGPLPA58 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  8088090200  5118138200  2.03 CGPLPA59 Preoperative treatment na 

ve WGS Pancreatic Cancer 100 14547364600  9617778600  3.82 CGPLPA67 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  8222177400  5351172600  2.12 CGPLPA69 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  7899181400  5006114800  1.99 CGPLPA71 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  7349620400  4955417400  1.97 CGPLPA74 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  6666371400  4571394200  1.81 CGPLPA76 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  9755658600  6412606800  2.54 CGPLPA85 Preoperative treatment na 

ve WGS Pancreatic Cancer 100 10856223000  7309498600  2.90 CGPLPA86 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  8744365400  5514523200  2.19 CGPLPA92 Preoperative treatment na 

ve WGS Pancreatic Cancer 100  8073791200  5390492800  2.14 CGPLPA93 Preoperative treatment na 

ve WGS Pancreatic Cancer 100 10390273000  7186589400  2.85 CGPLPA94 Preoperative treatment na 

ve WGS Pancreatic Cancer 100 11060347600  7641336400  3.03 CGPLPA95 Preoperative treatment na 

ve WGS Pancreatic Cancer 100 12416627200  7206503800  2.86 CGST102 Preoperative treatment na 

ve WGS Gastric cancer 100  6637004600  4545072600  1.80 CGST11 Preoperative treatment na 

ve WGS Gastric cancer 100  9718427800  6259679600  2.48 CGST110 Preoperative treatment na 

ve WGS Gastric cancer 100  9319661600  6359317400  2.52 CGST114 Preoperative treatment na 

ve WGS Gastric cancer 100  6865213000  4841171600  1.92 CGST13 Preoperative treatment na 

ve WGS Gastric cancer 100  9284554800  6360843800  2.52 CGST131 Preoperative treatment na 

ve WGS Gastric cancer 100  5924382000  3860677200  1.53 CGST141 Preoperative treatment na 

ve WGS Gastric cancer 100  8486380800  5860491000  2.33 CGST16 Preoperative treatment na 

ve WGS Gastric cancer 100 13820725800  9377828000  3.72 CGST18 Preoperative treatment na 

ve WGS Gastric cancer 100  7781288000  5278862400  2.09 CGST21 Preoperative treatment na 

ve WGS Gastric cancer 100  7171165400  4103970800  1.63 CGST26 Preoperative treatment na 

ve WGS Gastric cancer 100  8983961800  6053405600  2.40 CGST26 Preoperative treatment na 

ve WGS Gastric cancer 100  9683035400  6745116400  2.68 CGST30 Preoperative treatment na 

ve WGS Gastric cancer 100  8684086600  5741416000  2.28 CGST32 Preoperative treatment na 

ve WGS Gastric cancer 100  8568194600  5783369200  2.29 CGST33 Preoperative treatment na 

ve WGS Gastric cancer 100  9351699600  6448718400  2.56 CGST38 Preoperative treatment na 

ve WGS Gastric cancer 100  8409876400  5770989200  2.29 CGST39 Preoperative treatment na 

ve WGS Gastric cancer 100 10573763000  7597016000  3.01 CGST41 Preoperative treatment na 

ve WGS Gastric cancer 100  9434854200  6609415400  2.62 CGST45 Preoperative treatment na 

ve WGS Gastric cancer 100  8203868600  5625223000  2.23 CGST47 Preoperative treatment na 

ve WGS Gastric cancer 100  8938597600  6178990600  2.45 CGST48 Preoperative treatment na 

ve WGS Gastric cancer 100  9106628800  6517085200  2.59 CGST53 Preoperative treatment na 

ve WGS Gastric cancer 100  9005374200  5854996200  2.32 CGST58 Preoperative treatment na 

ve WGS Gastric cancer 100 10020368600  6133458400  2.43 CGST67 Preoperative treatment na 

ve WGS Gastric cancer 100  9198135600  5911071000  2.35 CGST77 Preoperative treatment na 

ve WGS Gastric cancer 100  8228789400  5119116800  2.03 CGST80 Preoperative treatment na 

ve WGS Gastric cancer 100 10596963400  7283152800  2.89 CGST81 Preoperative treatment na 

ve WGS Gastric cancer 100 8494881200  5838064000  2.32

TABLE 5 Appendix E: High coverage whole genome cfDNA analyses of healthy individuals and lung cancer patients Correlation of Correlation of Fragment GC Corrected Correlation Ratio Profile Fragment of Fragment Correlation Fragment Size Ratio Profile Ratio Profile of Fragment (bp) to Median to Median to Median Ratio Profile Median Fragment Fragment Fragment to cfDNA Ratio Profile Ratio Profile Ratio Lymphocyte Patient Analysis Stage at Fragment of Healthy of Healthy Profile of Nucleosome Patient Type Type Timepoint Diagnosis Size (bp) Individuals Individuals Lymphocytes Distances CGPLH75 Healthy WGS Preoperative NA 168 0.977 0.952 0.920 −0.886 treatment na 

ve CGPLH77 Healthy WGS Preoperative NA 166 0.970 0.960 0.904 −0.912 treatment na 

ve CGPLH80 Healthy WGS Preoperative NA 168 0.955 0.949 0.960 −0.917 treatment na 

ve CGPLH81 Healthy WGS Preoperative NA 167 0.949 0.953 0.869 −0.883 treatment na 

ve CGPLH82 Healthy WGS Preoperative NA 166 0.969 0.949 0.954 −0.917 treatment na 

ve CGPLH83 Healthy WGS Preoperative NA 167 0.949 0.939 0.919 −0.904 treatment na 

ve CGPLH84 Healthy WGS Preoperative NA 168 0.967 0.948 0.951 −0.913 treatment na 

ve CGPLH52 Healthy WGS Preoperative NA 167 0.946 0.968 0.952 −0.924 treatment na 

ve CGPLH35 Healthy WGS Preoperative NA 166 0.981 0.973 0.945 −0.921 treatment na 

ve CGPLH37 Healthy WGS Preoperative NA 168 0.968 0.970 0.951 −0.922 treatment na 

ve CGPLH54 Healthy WGS Preoperative NA 167 0.968 0.976 0.948 −0.925 treatment na 

ve CGPLH55 Healthy WGS Preoperative NA 166 0.947 0.964 0.948 −0.917 treatment na 

ve CGPLH48 Healthy WGS Preoperative NA 168 0.959 0.965 0.960 −0.923 treatment na 

ve CGPLH50 Healthy WGS Preoperative NA 167 0.960 0.968 0.952 −0.921 treatment na 

ve CGPLH36 Healthy WGS Preoperative NA 168 0.955 0.954 0.955 −0.919 treatment na 

ve CGPLH42 Healthy WGS Preoperative NA 167 0.973 0.963 0.948 −0.918 treatment na 

ve CGPLH43 Healthy WGS Preoperative NA 166 0.952 0.958 0.953 −0.928 treatment na 

ve CGPLH59 Healthy WGS Preoperative NA 168 0.970 0.965 0.951 −0.925 treatment na 

ve CGPLH45 Healthy WGS Preoperative NA 168 0.965 0.950 0.949 −0.911 treatment na 

ve CGPLH47 Healthy WGS Preoperative NA 167 0.952 0.944 0.954 −0.921 treatment na 

ve CGPLH46 Healthy WGS Preoperative NA 168 0.966 0.965 0.953 −0.923 treatment na 

ve CGPLH63 Healthy WGS Preoperative NA 168 0.977 0.968 0.939 −0.920 treatment na 

ve CGPLH51 Healthy WGS Preoperative NA 168 0.935 0.955 0.957 −0.914 treatment na 

ve CGPLH57 Healthy WGS Preoperative NA 169 0.965 0.954 0.955 −0.917 treatment na 

ve CGPLH49 Healthy WGS Preoperative NA 168 0.958 0.951 0.950 −0.924 treatment na 

ve CGPLH56 Healthy WGS Preoperative NA 166 0.940 0.957 0.959 −0.911 treatment na 

ve CGPLH64 Healthy WGS Preoperative NA 169 0.960 0.940 0.949 −0.918 treatment na 

ve CGPLH78 Healthy WGS Preoperative NA 166 0.956 0.936 0.958 −0.911 treatment na 

ve CGPLH79 Healthy WGS Preoperative NA 168 0.960 0.957 0.953 −0.917 treatment na 

ve CGPLH76 Healthy WGS Preoperative NA 167 0.969 0.965 0.953 −0.917 treatment na 

ve CGPLLU175 Lung WGS Preoperative I 165 0.316 0.284 0.244 −0.262 Cancer treatment na 

ve CGPLLU180 Lung WGS Preoperative I 166 0.907 0.846 0.826 −0.819 Cancer treatment na 

ve CGPLLU198 Lung WGS Preoperative I 166 0.972 0.946 0.928 −0.911 Cancer treatment na 

ve CGPLLU202 Lung WGS Preoperative I 163 0.821 0.605 0.905 −0.843 Cancer treatment na 

ve CGPLLU165 Lung WGS Preoperative II 163 0.924 0.961 0.815 −0.851 Cancer treatment na 

ve CGPLLU209 Lung WGS Preoperative II 163 0.578 0.526 0.513 −0.534 Cancer treatment na 

ve CGPLLU147 Lung WGS Preoperative III 166 0.953 0.919 0.939 −0.912 Cancer treatment na 

ve CGPLLU206 Lung WGS Preoperative III 158 0.488 0.343 0.460 −0.481 Cancer treatment na 

ve

TABLE 6 APPENDIX F: Monitoring response to therapy using whole genome analyses of cfDNA fragmentation profiles and targeted mutations analyses Correlation of Correlation of Fragment Ratio Fragment Ratio Progression Profile to Median Profile to Maximum from Fragrant Ratio Lymphocyte Mutant Patient Analysis Survival Profile of Healthy Nucleosne Allele Patient Type Type Timepoint Stage (months) Individuals Distances Targeted Mutation Fraction CGPLLU14 Lung Targeted Pre-treatment, IV 15.4 0.941 −0.841 EGFR 861DQ  0.89% Cancer Mutation Day −38 Analysis and WGS CGPLLU14 Lung Targeted Pre-treatment, IV 15.4 0.933 −0.833 EGFR 861DQ  0.18% Cancer Mutation Day −16 Analysis and WGS CGPLLU14 Lung Targeted Pre-treatment, IV 15.4 0.908 −0.814 EGFR 719G>S  0.49% Cancer Mutation Day −3 Analysis and WGS CGPLLU14 Lung Targeted Pre-treatment, IV 15.4 0.883 −0.752 EGFR 861DQ  1.39% Cancer Mutation Day 0 Analysis and WGS CGPLLU14 Lung Targeted Post-treatment, IV 15.4 0.820 −0.692 EGFR 719G>S  1.05% Cancer Mutation Day 0.33 Analysis and WGS CGPLLU14 Lung Targeted Post-treatment, IV 15.4 0.927 −0.887 EGFR 861DQ  0.00% Cancer Mutation Day 7 Analysis and WGS CGPLLU88 Lung Targeted Pre-treatment, IV 18.0 0.657 −0.584 EGFR 745KELREA>T  9.06% Cancer Mutation Day 0 Analysis and WGS CGPLLU88 Lung Targeted Post-treatment, IV 18.0 0.939 −0.799 EGFR 790T>M  0.15% Cancer Mutation Day 7 Analysis and WGS CGPLLU88 Lung Targeted Post-treatment, IV 18.0 0.946 −0.869 EGFR 745KELREA>T  0.93% Cancer Mutation Day 297 Analysis and WGS CGPLLU244 Lung Targeted Pre-treatment, IV  1.2 0.850 −0.706 EGFR 858DR  4.98% Cancer Mutation Day −7 Analysis and WGS CGPLLU244 Lung Targeted Pre-treatment, IV  1.2 0.867 −0.764 EGFR 62DR  3.41% Cancer Mutation Day −1 Analysis and WGS CGPLLU244 Lung Targeted Post-treatment, IV  1.2 0.703 −0.639 EGFR 858DR  5.57% Cancer Mutation Day 6 Analysis and WGS CGPLLU244 Lung Targeted Post-treatment, IV  1.2 0.659 −0.660 EGFR 858DR 11.80% Cancer Mutation Day 62 Analysis and WGS CGPLLU245 Lung Targeted Pre-treatment, IV  1.7 0.871 −0.724 EGFR 745KELREA* 10.60% Cancer Mutation Day −32 Analysis and WGS CGPLLU245 Lung Targeted Pre-treatment, IV  1.7 0.738 −0.608 EGFR 745KELREA* 14.10% Cancer Mutation Day 0 Analysis and WGS CGPLLU245 Lung Targeted Post-treatment, IV  1.7 0.731 −0.559 EGFR 745KELREA*  8.56% Cancer Mutation Day 7 Analysis and WGS CGPLLU245 Lung Targeted Post-treatment, IV  1.7 0.613 −0.426 EGFR 745KELREA* 10.69% Cancer Mutation Day 21 Analysis and WGS CGPLLU246 Lung Targeted Pre-treatment, IV  1.3 0.897 −0.757 EGFR 790T>M  0.49% Cancer Mutation Day −21 Analysis and WGS CGPLLU246 Lung Targeted Pre-treatment, IV  1.3 0.469 −0.376 EGFR 858DR  6.17% Cancer Mutation Day 0 Analysis and WGS CGPLLU246 Lung Targeted Post-treatment, IV  1.3 0.874 −0.746 EGFR 858DR  1.72% Cancer Mutation Day 9 Analysis and WGS CGPLLU246 Lung Targeted Post-treatment, IV  1.3 0.775 −0.665 EGFR 858DR  5.29% Cancer Mutation Day 42 Analysis and WGS CGPLLU86 Lung Targeted Pre-treatment, IV 12.4 0.817 −0.630 EGFR 746ELREATS>D  0.00% Cancer Mutation Day 0 Analysis and WGS CGPLLU86 Lung Targeted Post-treatment, IV 12.4 0.916 −0.811 EGFR 746ELREATS>D  0.19% Cancer Mutation Day 0.5 Analysis and WGS CGPLLU86 Lung Targeted Post-treatment, IV 12.4 0.859 −0.694 EGFR 746ELREATS>D  0.00% Cancer Mutation Day 7 Analysis and WGS CGPLLU86 Lung Targeted Post-treatment, IV 12.4 0.932 −0.848 EGFR 746ELREATS>D  0.00% Cancer Mutation Day 17 Analysis and WGS CGPLLU89 Lung Targeted Pre-treatment, IV  6.7 0.864 −0.729 EGFR 747ELREATS>-  0.42% Cancer Mutation Day 0 Analysis and WGS CGPLLU89 Lung Targeted Post-treatment, IV  6.7 0.908 −0.803 EGFR 747ELREATS>-  0.20% Cancer Mutation Day 7 Analysis and WGS CGPLLU89 Lung Targeted Post-treatment, IV  6.7 0.853 −0.881 EGFR 747ELREATS>-  0.00% Cancer Mutation Day 22 Analysis and WGS CGLU316 Lung Targeted Pre-treatment, IV  1.4 0.331 −0.351 EGFR L861Q 15.72% Cancer Mutation Day −53 Analysis and WGS CGLU316 Lung Targeted Pre-treatment, IV  1.4 0.225 −0.253 EGFR L861Q 45.67% Cancer Mutation Day −4 Analysis and WGS CGLU316 Lung Targeted Post-treatment, IV  1.4 0.336 −0.364 EGFR G719A 33.38% Cancer Mutation Day 18 Analysis and WGS CGLU316 Lung Targeted Post-treatment, IV  1.4 0.340 −0.364 EGFR L861Q 66.01% Cancer Mutation Day 87 Analysis and WGS CGLU344 Lung Targeted Pre-treatment, IV Ongoing 0.935 −0.818 EGFR_E746_A750del  0.00% Cancer Mutation Day −21 Analysis and WGS CGLU344 Lung Targeted Pre-treatment, IV Ongoing 0.919 −0.774 EGFR_E746_A750del  0.22% Cancer Mutation Day 0 Analysis and WGS CGLU344 Lung Targeted Post-treatment, IV Ongoing 0.953 −0.860 EGFR_E746_A750del  0.40% Cancer Mutation Day 0.1875 Analysis and WGS CGLU344 Lung Targeted Post-treatment, IV Ongoing 0.944 −0.832 EGFR_E746_A750del  0.00% Cancer Mutation Day 59 Analysis and WGS CGLU369 Lung Targeted Pre-treatment, IV  7.5 0.825 −0.826 EGFR L858R 20.61% Cancer Mutation Day −2 Analysis and WGS CGLU369 Lung Targeted Post-treatment, IV  7.5 0.950 −0.903 EGFR L858R  0.22% Cancer Mutation Day 12 Analysis and WGS CGLU369 Lung Targeted Post-treatment, IV  7.5 0.945 −0.889 EGFR L858R  0.16% Cancer Mutation Day 68 Analysis and WGS CGLU369 Lung Targeted Post-treatment, IV  7.5 0.886 −0.883 EGFR L858R  0.10% Cancer Mutation Day 110 Analysis and WGS CGLU373 Lung Targeted Pre-treatment, IV Ongoing 0.922 −0.804 EGFR_E746_A750del  0.82% Cancer Mutation Day −2 Analysis and WGS CGLU373 Lung Targeted Post-treatment, IV Ongoing 0.959 −0.853 EGFR_E746_A750del  0.00% Cancer Mutation Day 0.125 Analysis and WGS CGLU373 Lung Targeted Post-treatment, IV Ongoing 0.967 −0.886 EGFR_E746_A750del  0.15% Cancer Mutation Day 7 Analysis and WGS CGLU373 Lung Targeted Post-treatment, IV Ongoing 0.951 −0.890 EGFR_E746_A750del  0.00% Cancer Mutation Day 47 Analysis and WGS CGPLLU13 Lung Targeted Pre-treatment, IV  1.5 0.425 −0.400 EGFR_E746_A750del  7.66% Cancer Mutation Day −2 Analysis and WGS CGPLLU13 Lung Targeted Post-treatment, IV  1.5 0.272 −0.257 EGFR_E746_A750del 13.10% Cancer Mutation Day 5 Analysis and WGS CGPLLU13 Lung Targeted Post-treatment, IV  1.5 0.584 −0.536 EGFR_E746_A750del  6.09% Cancer Mutation Day 28 Analysis and WGS CGPLLU13 Lung Targeted Post-treatment, IV  1.5 0.530 −0.513 EGFR_E746_A750del  9.28% Cancer Mutation Day 91 Analysis and WGS CGPLLU264 Lung Targeted Pre-treatment, IV Ongoing 0.946 −0.824 EGFR D761N  0.00% Cancer Mutation Day −1 Analysis and WGS CGPLLU264 Lung Targeted Post-treatment IV Ongoing 0.927 −0.788 EGFR D761N  0.16% Cancer Mutation Day 6 Analysis and WGS CGPLLU264 Lung Targeted Post-treatment, IV Ongoing 0.962 −0.856 EGFR D761N  0.00% Cancer Mutation Day 27 Analysis and WGS CGPLLU264 Lung Targeted Post-treatment, IV Ongoing 0.960 −0.894 EGFR D761N  0.00% Cancer Mutation Day 69 Analysis and WGS CGPLLU265 Lung Targeted Pre-treatment, IV Ongoing 0.953 −0.859 EGFR L858R  0.21% Cancer Mutation Day 0 Analysis and WGS CGPLLU265 Lung Targeted Post-treatment, IV Ongoing 0.949 −0.842 EGFR L858R  0.21% Cancer Mutation Day 3 Analysis and WGS CGPLLU265 Lung Targeted Post-treatment, IV Ongoing 0.955 −0.844 EGFR 7790M  0.21% Cancer Mutation Day 7 Analysis and WGS CGPLLU265 Lung Targeted Post-treatment, IV Ongoing 0.946 −0.825 EGFR L858R  0.00% Cancer Mutation Day 84 Analysis and WGS CGPLLU266 Lung Targeted Pre-treatment, IV  9.6 0.961 −0.904 NA  0.00% Cancer Mutation Day 0 Analysis and WGS CGPLLU266 Lung Targeted Post-treatment, IV  9.6 0.959 −0.886 NA  0.00% Cancer Mutation Day 16 Analysis and WGS CGPLLU266 Lung Targeted Post-treatment, IV  9.6 0.961 −0.880 NA  0.00% Cancer Mutation Day 83 Analysis and WGS CGPLLU266 Lung Targeted Post-treatment, IV  9.6 0.958 −0.855 NA  0.00% Cancer Mutation Day 328 Analysis and WGS CGPLLU267 Lung Targeted Pre-treatment, IV  3.9 0.919 −0.863 EGFR L858R  1.93% Cancer Mutation Day −1 Analysis and WGS CGPLLU267 Lung Targeted Post-treatment, IV  3.9 0.863 −0.889 EGFR L858R  0.14% Cancer Mutation Day 34 Analysis and WGS CGPLLU267 Lung Targeted Post-treatment, IV  3.9 0.962 −0.876 EGFR L858R  0.38% Cancer Mutation Day 90 Analysis and WGS CGPLLU269 Lung Targeted Pre-treatment, IV Ongoing 0.951 −0.864 EGFR L858R  0.10% Cancer Mutation Day 0 Analysis and WGS CGPLLU269 Lung Targeted Post-treatment IV Ongoing 0.941 −0.894 EGFR L858R  0.00% Cancer Mutation Day 9 Analysis and WGS CGPLLU269 Lung Targeted Post-treatment, IV Ongoing 0.957 −0.876 EGFR L858R  0.00% Cancer Mutation Day 28 Analysis and WGS CGPLLU271 Lung Targeted Pre-treatment, IV  8.2 0.371 −0.284 EGFR_E746_A750del  3.36% Cancer Mutation Day 0 Analysis and WGS CGPLLU271 Lung Targeted Post-treatment, IV  8.2 0.947 −0.826 EGFR_E746_A750del  0.17% Cancer Mutation Day 6 Analysis and WGS CGPLLU271 Lung Targeted Post-treatment, IV  8.2 0.952 −0.839 EGFR_E746_A750del  0.00% Cancer Mutation Day 20 Analysis and WGS CGPLLU271 Lung Targeted Post-treatment, IV  8.2 0.944 −0.810 EGFR_E746_A750del  0.00% Cancer Mutation Day 104 Analysis and WGS CGPLLU271 Lung Targeted Post-treatment, IV  8.2 0.950 −0.831 EGFR_E746_A750del  0.44% Cancer Mutation Day 259 Analysis and WGS CGPLLU43 Lung Targeted Pre-treatment, IV Ongoing 0.944 −0.903 NA  0.00% Cancer Mutation Day −1 Analysis and WGS CGPLLU43 Lung Targeted Post-treatment, IV Ongoing 0.956 −0.899 NA  0.00% Cancer Mutation Day 6 Analysis and WGS CGPLLU43 Lung Targeted Post-treatment, IV Ongoing 0.959 −0.901 NA  0.00% Cancer Mutation Day 27 Analysis and WGS CGPLLU43 Lung Targeted Post-treatment, IV Ongoing 0.965 −0.896 NA  0.00% Cancer Mutation Day 83 Analysis and WGS

TABLE 7 APPENDIX G: Whole genome cfDNA analyses in healthy individuals and cancer patients Correlation of GC Correlation of Corrected Fragnent Fraction of Median Fragment Ratio Ratio Profile to Reads Mutant Alelle cfDNA Profile to Median Median Fragnent Mapped to Detected Detected Fraction Detected Patient Stage at Fragment Fragnent Profile of Ratio Profile of Mitochondrial DELFI using DELFI using DELFI using Targeted Patient Type Analysis Type Timepoint Diagnosis Size (bp) Healthy Individuals Healthy Individuals Genome Score (95% specificity) (98% specificity) sequencing CGCRC291 Colorectal Targeted Mutation Preoperative IV 163   0.1972 0.5268 0.0484% 0.9976 Y Y 22.85% Cancer Analysis and WGS treatment na 

ve CGCRC292 Colorectal Targeted Mutation Preoperative IV 166   0.7804 0.8835 0.0270% 0.7299 Y N  1.41% Cancer Analysis and WGS treatment na 

ve CGCRC293 Colorectal Targeted Mutation Preoperative IV 166   0.9335 0.9206 0.0748% 0.5234 N N  0.35% Cancer Analysis and WGS treatment na 

ve CGCRC294 Colorectal Targeted Mutation Preoperative II 166   0.6531 0.8904 0.0188% 0.8757 Y Y  0.17% Cancer Analysis and WGS treatment na 

ve CGCRC296 Colorectal Targeted Mutation Preoperative II 166   0.8161 0.8695 0.0369% 0.9951 Y Y ND Cancer Analysis and WGS treatment na 

ve CGCRC299 Colorectal Targeted Mutation Preoperative I 162   0.7325 0.9268 0.0392% 0.9648 Y Y ND Cancer Analysis and WGS treatment na 

ve CGCRC300 Colorectal Targeted Mutation Preoperative I 167   0.9382 0 9303 0.0235% 0.4447 N N ND Cancer Analysis and WGS treatment na 

ve CGCRC301 Colorectal Targeted Mutation Preoperative I 165   0.8252 0.9151 0.0310% 0.2190 N N  0.21% Cancer Analysis and WGS treatment na 

ve GGCRC302 Colorectal Targeted Mutation Preoperative II 163   0.7499 0.9243 0.0112% 0.9897 Y Y  0.12% Cancer Analysis and WGS treatment na 

ve CGCRC304 Colorectal Targeted Mutation Preoperative II 162   0.4642 0.9360 0.0093% 0.9358 Y Y  0.27% Cancer Analysis and WGS treatment na 

ve CGCRC305 Colorectal Targeted Mutation Preoperative II 165   0.8909 0.9250 0.0120% 0.8988 Y Y  0.19% Cancer Analysis and WGS treatment na 

ve CGCRC306 Colorectal Targeted Mutation Preoperative II 165   0.8523 0.8186 0.0781% 0.9466 Y Y  8.02% Cancer Analysis and WGS treatment na 

ve CGCRC307 Colorectal Targeted Mutation Preoperative II 165   0.9140 0.9342 0.0181% 0.7042 Y N  0.56% Cancer Analysis and WGS treatment na 

ve CGCRC308 Colorectal Targeted Mutation Preoperative III 165   0.8734 0.9324 0.0078% 0.9082 Y Y  0.11% Cancer Analysis and WGS treatment na 

ve CGCRC311 Colorectal Targeted Mutation Preoperative I 166   0.8535 0.9156 0.0173% 0.1867 N N ND Cancer Analysis and WGS treatment na 

ve CGCRC315 Colorectal Targeted Mutation Preoperative III 167   0.6083 0.8846 0.0241% 0.6422 Y N  0.27% Cancer Analysis and WGS treatment na 

ve CGCRC316 Colorectal Targeted Mutation Preoperative III 161   0.1546 0.5879 0.0315% 0.9971 Y Y  6.52% Cancer Analysis and WGS treatment na 

ve CGCRC317 Colorectal Targeted Mutation Preoperative III 163   0.6242 0.8944 0.0184% 0.9855 Y Y  0.36% Cancer Analysis and WGS treatment na 

ve CGCRC318 Colorectal Targeted Mutation Preoperative I 166   0.8824 0.9140 0.0156% 0.5615 N N ND Cancer Analysis and WGS treatment na 

ve CGCRC319 Colorectal Targeted Mutation Preoperative III 160   0.5979 0.8230 0.1259% 0.9925 Y Y  0.11% Cancer Analysis and WGS treatment na 

ve CGCRC320 Colorectal Targeted Mutation Preoperative I 167   0.7949 0.9101 0.0383% 0.8019 Y Y  0.64% Cancer Analysis and WGS treatment na 

ve CGCRC321 Colorectal Targeted Mutation Preoperative I 164   0.7804 0.9091 0.0829% 0.9759 Y Y  0.20% Cancer Analysis and WGS treatment na 

ve CGCRC333 Colorectal Targeted Mutation Preoperative IV 163   0.4263 0.4355 0.4284% 0.9974 Y Y 43.03% Cancer Analysis and WGS treatment na 

ve CGCRC336 Colorectal Targeted Mutation Preoperative IV 162   0.6466 0.6856 0.1154% 0.9887 Y Y 81.61% Cancer Analysis and WGS treatment na 

ve CGCRC338 Colorectal Targeted Mutation Preoperative IV 162   0.7740 0.7573 0.1436% 0.9976 Y Y 36.00% Cancer Analysis and WGS treatment na 

ve CGCRC341 Colorectal Targeted Mutation Preoperative IV 164   0.8995 0.9191 0.0197% 0.9670 Y Y ND Cancer Analysis and WGS treatment na 

ve CGCRC342 Colorectal Targeted Mutation Preoperative IV 158   0.2524 0.1845 0.1732% 0.9987 Y Y 30.72% Cancer Analysis and WGS treatment na 

ve CGPLBR100 Breast Targeted Mutation Preoperative III 166   0.9440 0.8946 0.1234% 0.8664 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLBR101 Breast Targeted Mutation Preoperative II 169   0.8864 0.9304 0.0709% 0.9385 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLBR102 Breast Targeted Mutation Preoperative II 169   0.9617 0.9345 0.4742% 0.9052 Y Y  0.25% Cancer Analysis and WGS treatment na 

ve CGPLBR103 Breast Targeted Mutation Preoperative II 168   0.9498 0.9251 0.0775% 0.5994 N N ND Cancer Analysis and WGS treatment na 

ve CGPLBR104 Breast Targeted Mutation Preoperative II 167   0.8490 0.9192 0.0532% 0.9950 Y Y  0.13% Cancer Analysis and WGS treatment na 

ve CGPLBR12 Breast WGS Preoperative III 164   0.8350 0.7760 0.1407% 0.7598 Y Y Cancer treatment na 

ve CGPLBR18 Breast WGS Preoperative III 163   0.8411 0.9534 0.0267% 0.3886 N N Cancer treatment na 

ve CGPLBR23 Breast WGS Preoperative II 166   0.9714 0.9312 0.0144% 0.1235 N N Cancer treatment na 

ve CGPLBR24 Breast WGS Preoperative II 156   0.8402 0.8766 0.0210% 0.7480 Y Y Cancer treatment na 

ve CGPLBR28 Breast WGS Preoperative III 166   0.9584 0.9120 0.1456% 0.9630 Y Y Cancer treatment na 

ve CGPLBR30 Breast WGS Preoperative II 161   0.6951 0.6611 0.0952% 0.9956 Y Y Cancer treatment na 

ve CGPLBR31 Breast WGS Preoperative II 167   0.9719 0.9556 0.0427% 0.2227 N N Cancer treatment na 

ve CGPLBR32 Breast WGS Preoperative II 166   0.9590 0.9229 0.0306% 0.9815 Y Y Cancer treatment na 

ve CGPLBR33 Breast WGS Preoperative II 166   0.9706 0.9432 0.0617% 0.2863 N N Cancer treatment na 

ve CGPLBR34 Breast WGS Preoperative II 163   0.8735 0.9425 0.0115% 0.1637 N N Cancer treatment na 

ve CGPLBR35 Breast WGS Preoperative II 168   0.9655 0.9348 0.1371% 0.5057 N N Cancer treatment na 

ve CGPLBR36 Breast WGS Preoperative II 169   0.9394 0.8884 0.0813% 0.4017 N N Cancer treatment na 

ve CGPLBR37 Breast WGS Preoperative II 167   0.9691 0.9496 0.0518% 0.0314 N N — Cancer treatment na 

ve CGPLBR38 Breast Targeted Mutation Preoperative I 165   0.9105 0.9349 0.1352% 0.8983 Y Y  0.53% Cancer Analysis and WGS treatment na 

ve CGPLBR40 Breast Targeted Mutation Preoperative III 167   0.9273 0.9244 0.0929% 0.9046 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLBR41 Breast Targeted Mutation Preoperative III 168   0.9626 0.9346 0.0544% 0.9416 Y Y  0.32% Cancer Analysis and WGS treatment na 

ve CGPLBR45 Breast WGS Preoperative II 164   0.9615 0.9286 0.0296% 0.3860 N N Cancer treatment na 

ve CGPLBR46 Breast WGS Preoperative III 168   0.9322 0.9005 0.0345% 0.7270 Y N Cancer treatment na 

ve CGPLBR47 Breast WGS Preoperative I 166   0.9461 0.9028 0.0591% 0.8247 Y Y — Cancer treatment na 

ve CGPLBR48 Breast Targeted Mutation Preoperative II 169   0.7686 0.8246 0.0504% 0.9973 Y Y  0.18% Cancer Analysis and WGS treatment na 

ve CGPLBR49 Breast Targeted Mutation Preoperative II 171   0.8867 0.7887 0.0377% 0.9946 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLBR50 Breast WGS Preoperative I 160   0.8593 0.9332 0.0137% 0.6820 Y N Cancer treatment na 

ve CGPLBR51 Breast WGS Preoperative II 165   0.9359 0.9160 0.0863% 0.6915 Y N Cancer treatment na 

ve CGPLBR52 Breast WGS Preoperative III 164   0.8688 0.9196 0.0165% 0.6390 Y N — Cancer treatment na 

ve CGPLBR55 Breast Targeted Mutation Preoperative III 165   0.9634 0.9341 0.0356% 0.9494 Y Y  0.68% Cancer Analysis and WGS treatment na 

ve CGPLBR56 Breast WGS Preoperative II 163   0.9469 0.9428 0.2025% 0.4700 N N — Cancer treatment na 

ve CGPLBR57 Breast Targeted Mutation Preoperative III 166   0.9672 0.9416 0.0902% 0.9090 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLBR59 Breast Targeted Mutation Preoperative I 168   0.9438 0.9130 0.0761% 0.5828 N N ND Cancer Analysis and WGS treatment na 

ve CGPLBR60 Breast WGS Preoperative II 167   0.9479 0.8916 0.0626% 0.8779 Y Y — Cancer treatment na 

ve CGPLBR61 Breast Targeted Mutation Preoperative II 165   0.9611 0.9422 0.0601% 0.4417 N N  0.44% Cancer Analysis and WGS treatment na 

ve CGPLBR63 Breast Targeted Mutation Preoperative II 168   0.9555 0.9132 0.0514% 0.8788 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLBR65 Breast WGS Preoperative II 167   0.9506 0.8970 0.0264% 0.9048 Y Y — Cancer treatment na 

ve CGPLBR68 Breast Targeted Mutation Preoperative III 163   0.9154 0.9532 0.0164% 0.7863 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLBR69 Breast Targeted Mutation Preoperative II 165   0.9460 0.9474 0.0279% 0.0600 N N ND Cancer Analysis and WGS treatment na 

ve CGPLBR70 Breast Targeted Mutation Preoperative II 168   0.9651 0.9388 0.0171% 0.6447 Y N  0.36% Cancer Analysis and WGS treatment na 

ve CGPLBR71 Breast Targeted Mutation Preoperative II 165   0.9577 0.9368 0.0271% 0.6706 Y N  0.10% Cancer Analysis and WGS treatment na 

ve CGPLBR72 Breast Targeted Mutation Preoperative II 167   0.9786 0.9640 0.0263% 0.6129 N N ND Cancer Analysis and WGS treatment na 

ve CGPLBR73 Breast Targeted Mutation Preoperative II 167   0.9576 0.9421 0.0142% 0.0746 N N  0.27% Cancer Analysis and WGS treatment na 

ve CGPLBR76 Breast Targeted Mutation Preoperative II 170   0.9410 0.9254 0.0775% 0.9334 Y Y  0.12% Cancer Analysis and WGS treatment na 

ve CGPLBR81 Breast WGS Preoperative II 170   0.9043 0.8193 0.0241% 0.9899 Y Y Cancer treatment na 

ve CGPLBR82 Breast Targeted Mutation Preoperative I 166   0.9254 0.9288 0.1640% 0.9834 Y Y  0.12% Cancer Analysis and WGS treatment na 

ve CGPLBR83 Breast Targeted Mutation Preoperative II 169   0.9451 0.9138 0.0419% 0.9810 Y Y  0.28% Cancer Analysis and WGS treatment na 

ve CGPLBR84 Breast WGS Preoperative III 169   0.9315 0.8659 0.0274% 0.9901 Y Y — Cancer treatment na 

ve CGPLBR87 Breast Targeted Mutation Preoperative II 166   0.9154 0.8797 0.0294% 0.9968 Y Y 0.45% Cancer Analysis and WGS treatment na 

ve CGPLBR88 Breast Targeted Mutation Preoperative II 169   0.9370 0.8547 0.0181% 0.9988 Y Y  0.38% Cancer Analysis and WGS treatment na 

ve CGPLBR90 Breast WGS Preoperative II 169   0.9002 0.8330 0.0417% 0.9667 Y Y — Cancer treatment na 

ve CGPLBR91 Breast Targeted Mutation Preoperative III 164   0.7955 0.9408 0.0799% 0.8710 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLBR92 Breast Targeted Mutation Preoperative II 162   0.6774 0.8835 0.1042% 0.9866 Y Y  0.20% Cancer Analysis and WGS treatment na 

ve CGPLBR93 Breast Targeted Mutation Preoperative II 164   0.8773 0.9072 0.0352% 0.7253 Y N ND Cancer Analysis and WGS treatment na 

ve CGPLH189 Healthy WGS Preoperative NA 168   0.9325 0.8947 0.0591% 0.1748 N N treatment na 

ve CGPLH190 Healthy WGS Preoperative NA 167   0.9433 0.9369 0.1193% 0.5168 N N treatment na 

ve CGPLH192 Healthy WGS Preoperative NA 167   0.9646 0.9487 0.0276% 0.0178 N N treatment na 

ve CGPLH193 Healthy WGS Preoperative NA 167   0.9423 0.9442 0.0420% 0.5794 N N treatment na 

ve CGPLH194 Healthy WGS Preoperative NA 168   0.9567 0.9289 0.0407% 0.1616 N N treatment na 

ve CGPLH196 Healthy WGS Preoperative NA 167   0.9709 0.9512 0.0266% 0.0999 N N treatment na 

ve CGPLH197 Healthy WGS Preoperative NA 166   0.9605 0.9416 0.0334% 0.4699 N N treatment na 

ve CGPLH198 Healthy WGS Preoperative NA 167   0.9238 0.9457 0.0302% 0.6571 Y N treatment na 

ve CCPCH199 Healthy WGS Preoperative NA 165   0.9618 0.9439 0.0170% 0.5564 N N treatment na 

ve CGPLH200 Healthy WGS Preoperative NA 167   0.9183 0.9391 0.0362% 0.3833 N N treatment na 

ve CGPLH2O1 Healthy WGS Preoperative NA 168   0.9548 0.9180 0.0470% 0.8395 r Y treatment na 

ve CGPLH2O2 Healthy WGS Preoperative NA 168   0.9471 0.9436 0.0501% 0.1088 N N treatment na 

ve CGPLH2O3 Healthy WGS Preoperative NA 167   0.9534 0.9575 0.0455% 0.2485 N N treatment na 

ve CGPLH2O5 Healthy WGS Preoperative NA 168   0.9075 0.9283 0.0409% 0.4401 N N treatment na 

ve CGPLH2O6 Healthy WGS Preoperative NA 168   0.9422 0.9409 0.0371% 0.2706 N N treatment na 

ve CGPLH2O9 Healthy WGS Preoperative NA 169   0.9556 0.9367 0.0427% 0.2213 N N treatment na 

ve CGPLH210 Healthy WGS Preoperative NA 169   0.9447 0.9181 0.0279% 0.3500 N N treatment na 

ve CGPLH211 Healthy WGS Preoperative NA 169   0.9538 0.9410 0.0317% 0.1752 N N treatment na 

ve CGPLH300 Healthy WGS Preoperative NA 168   0.9019 0.9200 0.0397% 0.0226 N N treatment na 

ve CGPLH307 Healthy WGS Preoperative NA 168   0.9576 0.9167 0.0388% 0.1789 N N treatment na 

ve CGPLI1308 Healthy WGS Preoperative NA 168   0.9481 0.9352 0.0311% 0.0185 N N treatment na 

ve CGPLH309 Healthy WGS Preoperative NA 168   0.9672 0.9451 0.0226% 0.0441 N N treatment na 

ve CGPLH310 Healthy WGS Preoperative NA 165   0.9547 0.9527 0.0145% 0.7135 Y N treatment na 

ve CGPLH311 Healthy WGS Preoperative NA 167   0.9302 0.9348 0.0202% 0.2589 N N treatment na 

ve CGPLH314 Healthy WGS Preoperative NA 167   0.9482 0.9491 0.0212% 0.1632 N N treatment na 

ve CGPLH315 Healthy WGS Preoperative NA 167   0.8659 0.9427 0.0071% 0.3450 N N treatment na 

ve CGPLH316 Healthy WGS Preoperative NA 165   0.9374 0.9552 0.0191% 0.4697 N N treatment na 

ve CGPLH317 Healthy WGS Preoperative NA 169   0.9542 0.9352 0.0232% 0.1330 N N treatment na 

ve CGPLH319 Healthy WGS Preoperative NA 167   0.9578 0.9189 0.0263% 0.2232 N N treatment na 

ve CGPLH320 Healthy WGS Preoperative NA 164   0.8913 0.9166 0.0222% 0.1095 N N treatment na 

ve CGPLH322 Healthy WGS Preoperative NA 167   0.8751 0.9411 0.0248% 0.0749 N N treatment na 

ve CGPLH324 Healthy WGS Preoperative NA 169   0.9519 0.9133 0.0402% 0.0126 N N treatment na 

ve CGPLH325 Healthy WGS Preoperative NA 167   0.9124 0.9202 0.0711% 0.0102 N N treatment na 

ve CGPLH326 Healthy WGS Preoperative NA 166   0.9574 0.9408 0.0213% 0.0475 N N treatment na 

ve CGPLH327 Healthy WGS Preoperative NA 168   0.9533 0.9071 0.1275% 0.4891 N N treatment na 

ve CGPLH328 Healthy WGS Preoperative NA 166   0.9643 0.9332 0.0256% 0.0234 N N treatment na 

ve CGPLH329 Healthy WGS Preoperative NA 167   0.9609 0.9396 0.0269% 0.0139 N N treatment na 

ve CGPLH330 Healthy WGS Preoperative NA 167   0.9118 0.9403 0.0203% 0.2642 N N treatment na 

ve CGPLH331 Healthy WGS Preoperative NA 166   0.9679 0.9377 0.0314% 0.0304 N N treatment na 

ve CGPLH333 Healthy WGS Preoperative NA 169   0.9474 0.9132 0.0350% 0.1633 N N treatment na 

ve CGPLH335 Healthy WGS Preoperative NA 167   0.8909 0.9333 0.0285% 0.0096 N N treatment na 

ve CGPLH336 Healthy WGS Preoperative NA 169   0.9248 0.9159 0.0159% 0.3872 N N treatment na 

ve CGPLH337 Healthy WGS Preoperative NA 167   0.9533 0.9262 0.0367% 0.2976 N N treatment na 

ve CGPLH338 Healthy WGS Preoperative NA 165   0.9388 0.9303 0.0103% 0.0431 N N treatment na 

ve CGPLH339 Healthy WGS Preoperative NA 167   0.9396 0.9338 0.0280% 0.0379 N N treatment na 

ve CGPLH340 Healthy WGS Preoperative NA 167   0.9488 0.9321 0.0210% 0.0379 N N treatment na 

ve CGPLH341 Healthy WGS Preoperative NA 166   0.9533 0.9187 0.0448% 0.1775 N N treatment na 

ve CGPLH342 Healthy WGS Preoperative NA 166   0.7858 0.8986 0.0283% 0.0904 N N treatment na 

ve CGPLH343 Healthy WGS Preoperative NA 167   0.9421 0.9067 0.0632% 0.0160 N N treatment na 

ve CGPLH344 Healthy WGS Preoperative NA 169   0.9192 0.8998 0.0257% 0.0120 N N treatment na 

ve CGPLH345 Healthy WGS Preoperative NA 169   0.9345 0.9107 0.0445% 0.0031 N N treatment na 

ve CGPLH346 Healthy WGS Preoperative NA 169   0.9475 0.9074 0.0208% 0.0686 N N treatment na 

ve CGPLH350 Healthy WGS Preoperative NA 171   0.9570 0.9288 0.0264% 0.0071 N N treatment na 

ve CGPLH351 Healthy WGS Preoperative NA 168   0.8176 0.9294 0.0223% 0.0207 N N treatment na 

ve CGPLH352 Healthy WGS Preoperative NA 168   0.9521 0.9190 0.0613% 0.0512 N N treatment na 

ve CGPLH353 Healthy WGS Preoperative NA 167   0.9432 0.9130 0.0408% 0.0132 N N treatment na 

ve CGPLH354 Healthy WGS Preoperative NA 168   0.9481 0.9121 0.0318% 0.0082 N N treatment na 

ve CGPLH355 Healthy WGS Preoperative NA 167   0.9613 0.9308 0.0400% 0.6407 Y N treatment na 

ve CGPLH356 Healthy WGS Preoperative NA 168   0.9474 0.9312 0.0427% 0.2437 N N treatment na 

ve CGPLH357 Healthy WGS Preoperative NA 167   0.9255 0.9540 0.0217% 0.0070 N N treatment na 

ve CGPLH358 Healthy WGS Preoperative NA 167   0.7777 0.9372 0.0174% 0.1451 N N treatment na 

ve CGPLH360 Healthy WGS Preoperative NA 168   0.8500 0.8775 0.0395% 0.0048 N N treatment na 

ve CGPLH361 Healthy WGS Preoperative NA 167   0.9261 0.9283 0.0268% 0.1524 N N treatment na 

ve CGPLH362 Healthy WGS Preoperative NA 167   0.9236 0.9503 0.0309% 0.4832 N N treatment na 

ve CGPLH363 Healthy WGS Preoperative NA 167   0.9488 0.9187 0.0620% 0.0199 N N treatment na 

ve CGPLH364 Healthy WGS Preoperative NA 168   0.9311 0.9480 0.0282% 0.8719 Y Y treatment na 

ve CGPLH365 Healthy WGS Preoperative NA 165   0.9371 0.9051 0.1740% 0.9683 Y Y treatment na 

ve CGPLH366 Healthy WGS Preoperative NA 167   0.9536 0.9170 0.0344% 0.0952 N N treatment na 

ve CGPLH367 Healthy WGS Preoperative NA 166   0.8748 0.9181 0.0353% 0.1235 N N treatment na 

ve CGPLH368 Healthy WGS Preoperative NA 169   0.9490 0.9076 0.1073% 0.1252 N N treatment na 

ve CGPLH369 Healthy WGS Preoperative NA 167   0.9428 0.9541 0.0246% 0.2621 N N treatment na 

ve CGPLH370 Healthy WGS Preoperative NA 167   0.9642 0.9423 0.0410% 0.0989 N N treatment na 

ve CGPLH371 Healthy WGS Preoperative NA 168   0.9621 0.9414 0.0734% 0.2173 N N treatment na 

ve CGPLH380 Healthy WGS Preoperative NA 170   0.9662 0.9424 0.0523% 0.0126 N N treatment na 

ve CGPLH381 Healthy WGS Preoperative NA 169   0.9541 0.9501 0.0435% 0.0152 N N treatment na 

ve CGPLH382 Healthy WGS Preoperative NA 167   0.9380 0.9584 0.0340% 0.0326 N N treatment na 

ve CGPLH383 Healthy WGS Preoperative NA 168   0.9700 0.9407 0.0389% 0.0035 N N treatment na 

ve CGPLH384 Healthy WGS Preoperative NA 169   0.8061 0.9043 0.0207% 0.0258 N N treatment na 

ve CGPLH385 Healthy WGS Preoperative NA 167   0.8866 0.9246 0.0165% 0.0566 N N treatment na 

ve CGPLH386 Healthy WGS Preoperative NA 167   0.6920 0.8859 0.0502% 0.2677 N N treatment na 

ve CGPLH387 Healthy WGS Preoperative NA 169   0.9583 0.9223 0.0375% 0.0081 N N treatment na 

ve CGPLH388 Healthy WGS Preoperative NA 167   0.9348 0.9266 0.0527% 0.0499 N N treatment na 

ve CGPLH389 Healthy WGS Preoperative NA 168   0.9409 0.9035 0.0667% 0.6565 Y N treatment na 

ve CGPLH390 Healthy WGS Preoperative NA 167   0.9216 0.9182 0.0229% 0.0837 N N treatment na 

ve CGPLH391 Healthy WGS Preoperative NA 166   0.9334 0.9162 0.0223% 0.0716 N N treatment na 

ve CGPLH392 Healthy WGS Preoperative NA 167   0.9165 0.9014 0.0424% 0.1305 N N treatment na 

ve CGPLH393 Healthy WGS Preoperative NA 169   0.9256 0.9045 0.0407% 0.0037 N N treatment na 

ve CGPLH394 Healthy WGS Preoperative NA 167   0.9257 0.9292 0.0522% 0.1073 N N treatment na 

ve CGPLH395 Healthy WGS Preoperative NA 166   0.8611 0.9254 0.0424% 0.0171 N N treatment na 

ve CGPLH396 Healthy WGS Preoperative NA 167   0.7884 0.8928 0.0393% 0.0303 N N treatment na 

ve CGPLH398 Healthy WGS Preoperative NA 167   0.9463 0.9578 0.0242% 0.3195 N N treatment na 

ve CGPLH399 Healthy WGS Preoperative NA 169   0.8780 0.9195 0.0679% 0.0685 N N treatment na 

ve CGPLH400 Healthy WGS Preoperative NA 168   0.6862 0.9047 0.0300% 0.2103 N N treatment na 

ve CGPLH401 Healthy WGS Preoperative NA 167   0.9428 0.9339 0.0146% 0.0620 N N treatment na 

ve CGPLH402 Healthy WGS Preoperative NA 167   0.9353 0.8800 0.1516% 0.0395 N N treatment na 

ve CGPLH403 Healthy WGS Preoperative NA 168   0.9329 0.8829 0.0515% 0.0223 N N treatment na 

ve CGPLH404 Healthy WGS Preoperative NA 169   0.9402 0.8948 0.0528% 0.0027 N N treatment na 

ve CGPLH405 Healthy WGS Preoperative NA 166   0.9579 0.9204 0.0359% 0.0188 N N treatment na 

ve CGPLH406 Healthy WGS Preoperative NA 167   0.8188 0.8592 0.0667% 0.0206 N N treatment na 

ve CGPLH407 Healthy WGS Preoperative NA 169   0.9527 0.9099 0.0229% 0.0040 N N treatment na 

ve CGPLH408 Healthy WGS Preoperative NA 167   0.9584 0.9192 0.0415% 0.1257 N N treatment na 

ve CGPLH409 Healthy WGS Preoperative NA 168   0.9220 0.8950 0.0302% 0.0056 N N treatment na 

ve CGPLH410 Healthy WGS Preoperative NA 168   0.9102 0.9006 0.0453% 0.0019 N N treatment na 

ve CGPLH411 Healthy WGS Preoperative NA 167   0.9392 0.8857 0.0621% 0.0188 N N treatment na 

ve CGPLH412 Healthy WGS Preoperative NA 167   0.9561 0.9191 0.0140% 0.0417 N N treatment na 

ve CGPLH413 Healthy WGS Preoperative NA 167   0.9461 0.9145 0.0355% 0.0084 N N treatment na 

ve CGPLH414 Healthy WGS Preoperative NA 168   0.9258 0.9127 0.0290% 0.0284 N N treatment na 

ve CGPLH415 Healthy WGS Preoperative NA 169   0.9217 0.9025 0.0296% 0.0131 N N treatment na 

ve CGPLH416 Healthy WGS Preoperative NA 167   0.9672 0.9388 0.0198% 0.0645 N N treatment na 

ve CGPLH417 Healthy WGS Preoperative NA 168   0.9578 0.9192 0.0241% 0.0836 N N treatment na 

ve CGPLH418 Healthy WGS Preoperative NA 169   0.9376 0.9234 0.0306% 0.0052 N N treatment na 

ve CGPLH419 Healthy WGS Preoperative NA 167   0.9228 0.9295 0.0280% 0.0469 N N treatment na 

ve CGPLH420 Healthy WGS Preoperative NA 169   0.9164 0.9106 0.0187% 0.0420 N N treatment na 

ve CGPLH422 Healthy WGS Preoperative NA 166   0.9069 0.9006 0.0209% 0.0324 N N treatment na 

ve CGPLH423 Healthy WGS Preoperative NA 169   0.9606 0.9289 0.0832% 0.0139 N N treatment na 

ve CGPLH424 Healthy WGS Preoperative NA 167   0.9553 0.9265 0.1119% 0.0864 N N treatment na 

ve CGPLH425 Healthy WGS Preoperative NA 168   0.9722 0.9488 0.0722% 0.0156 N N treatment na 

ve CGPLH426 Healthy WGS Preoperative NA 168   0.9560 0.9080 0.0548% 0.1075 N N treatment na 

ve CGPLH427 Healthy WGS Preoperative NA 167   0.9594 0.9257 0.0182% 0.0470 N N treatment na 

ve CGPLH428 Healthy WGS Preoperative NA 167   0.9591 0.9272 0.0346% 0.0182 N N treatment na 

ve CGPLH429 Healthy WGS Preoperative NA 168   0.9358 0.8757 0.0593% 0.8143 Y Y treatment na 

ve CGPLH430 Healthy WGS Preoperative NA 167   0.9639 0.9307 0.0258% 0.0369 N N treatment na 

ve CGPLH431 Healthy WGS Preoperative NA 167   0.9570 0.9185 0.0234% 0.0174 N N treatment na 

ve CGPLH432 Healthy WGS Preoperative NA 168   0.9485 0.9082 0.0433% 0.0181 N N treatment na 

ve CGPLH434 Healthy WGS Preoperative NA 168   0.9671 0.9442 0.0297% 0.0060 N N treatment na 

ve CGPLH435 Healthy WGS Preoperative NA 170   0.9133 0.9097 0.0179% 0.0441 N N treatment na 

ve CGPLH436 Healthy WGS Preoperative NA 168   0.9360 0.9158 0.0290% 0.0958 N N treatment na 

ve CGPLH437 Healthy WGS Preoperative NA 170   0.9445 0.9245 0.0156% 0.0136 N N treatment na 

ve CGPLH438 Healthy WGS Preoperative NA 170   0.9537 0.9138 0.0169% 0.1041 N N treatment na 

ve CGPLH439 Healthy WGS Preoperative NA 171   0.9547 0.9028 0.0226% 0.0078 N N treatment na 

ve CGPLH440 Healthy WGS Preoperative NA 169   0.9562 0.9295 0.0330% 0.0867 N N treatment na 

ve CGPLH441 Healthy WGS Preoperative NA 167   0.9660 0.9430 0.0178% 0.0085 N N treatment na 

ve CGPLH442 Healthy WGS Preoperative NA 167   0.9569 0.9406 0.0169% 0.0562 N N treatment na 

ve CGPLH443 Healthy WGS Preoperative NA 170   0.9431 0.8801 0.0207% 0.5878 N N treatment na 

ve CGPLH444 Healthy WGS Preoperative NA 171   0.9429 0.9066 0.0464% 0.0097 N N treatment na 

ve CGPLH445 Healthy WGS Preoperative NA 171   0.9446 0.8750 0.0267% 0.1939 N N treatment na 

ve CGPLH446 Healthy WGS Preoperative NA 167   0.9502 0.9257 0.0281% 0.0340 N N treatment na 

ve CGPLH447 Healthy WGS Preoperative NA 169   0.9421 0.8968 0.0167% 0.0017 N N treatment na 

ve CGPLH448 Healthy WGS Preoperative NA 167   0.9553 0.9191 0.0401% 0.0389 N N treatment na 

ve CGPLH449 Healthy WGS Preoperative NA 167   0.9550 0.9254 0.0236% 0.0116 N N treatment na 

ve CGPLH450 Healthy WGS Preoperative NA 167   0.9572 0.9195 0.0331% 0.0597 N N treatment na 

ve CGPLH451 Healthy WGS Preoperative NA 169   0.9548 0.9167 0.0262% 0.0104 N N treatment na 

ve CGPLH452 Healthy WGS Preoperative NA 167   0.9498 0.8948 0.0480% 0.4722 N N treatment na 

ve CGPLH453 Healthy WGS Preoperative NA 166   0.9572 0.9339 0.0186% 0.3419 N N treatment na 

ve CGPLH455 Healthy WGS Preoperative NA 166   0.9626 0.9322 0.0455% 0.4536 N N treatment na 

ve CGPLH456 Healthy WGS Preoperative NA 168   0.9537 0.9098 0.0207% 0.0365 N N treatment na 

ve CGPLH457 Healthy WGS Preoperative NA 167   0.9429 0.9022 0.0298% 0.0364 N N treatment na 

ve CGPLH458 Healthy WGS Preoperative NA 167   0.9511 0.9275 0.0298% 0.1891 N N treatment na 

ve CGPLH459 Healthy WGS Preoperative NA 168   0.9609 0.9209 0.0281% 0.0371 N N treatment na 

ve CGPLH460 Healthy WGS Preoperative NA 168   0.9331 0.8863 0.0227% 0.1157 N N treatment na 

ve CGPLH463 Healthy WGS Preoperative NA 167   0.9506 0.9372 0.0130% 0.0865 N N treatment na 

ve CGPLH464 Healthy WGS Preoperative NA 170   0.9133 0.8511 0.0659% 0.2040 N N treatment na 

ve CGPLH465 Healthy WGS Preoperative NA 167   0.9251 0.9164 0.0325% 0.0124 N N treatment na 

ve CGPLH466 Healthy WGS Preoperative NA 167   0.9679 0.9408 0.0155% 0.1733 N N treatment na 

ve CGPLH467 Healthy WGS Preoperative NA 168   0.9273 0.9024 0.0229% 0.2303 N N treatment na 

ve CGPLH468 Healthy WGS Preoperative NA 167   0.8353 0.9345 0.0247% 0.5427 N N treatment na 

ve CGPLH469 Healthy WGS Preoperative NA 169   0.8225 0.8799 0.0201% 0.5351 N N treatment na 

ve CGPLH470 Healthy WGS Preoperative NA 168   0.9073 0.9228 0.0715% 0.0327 N N treatment na 

ve CGPLH471 Healthy WGS Preoperative NA 167   0.9354 0.9333 0.0150% 0.0406 N N treatment na 

ve CGPLH472 Healthy WGS Preoperative NA 166   0.8509 0.8915 0.0481% 0.6152 N N treatment na 

ve CGPLH473 Healthy WGS Preoperative NA 167   0.9206 0.9128 0.0443% 0.2995 N N treatment na 

ve CGPLH474 Healthy WGS Preoperative NA 168   0.8474 0.9245 0.0316% 0.6246 Y N treatment na 

ve CGPLH475 Healthy WGS Preoperative NA 167   0.9155 0.9233 0.0269% 0.0736 N N treatment na 

ve CGPLH476 Healthy WGS Preoperative NA 169   0.8807 0.9059 0.0236% 0.0143 N N treatment na 

ve CGPLH477 Healthy WGS Preoperative NA 169   0.9129 0.9376 0.0382% 0.1111 N N treatment na 

ve CGPLH478 Healthy WGS Preoperative NA 167   0.9588 0.9344 0.0256% 0.0828 N N treatment na 

ve CGPLH479 Healthy WGS Preoperative NA 167   0.9303 0.9207 0.0221% 0.0648 N N treatment na 

ve CGPLH480 Healthy WGS Preoperative NA 169   0.9522 0.9046 0.0672% 0.7473 Y N treatment na 

ve CGPLH481 Healthy WGS Preoperative NA 168   0.9568 0.9113 0.0311% 0.0282 N N treatment na 

ve CGPLH482 Healthy WGS Preoperative NA 168   0.9379 0.9336 0.0162% 0.0058 N N treatment na 

ve CGPLH483 Healthy WGS Preoperative NA 168   0.9518 0.9275 0.0251% 0.0495 N N treatment na 

ve CGPLH484 Healthy WGS Preoperative NA 166   0.9630 0.9366 0.0261% 0.0048 N N treatment na 

ve CGPLH485 Healthy WGS Preoperative NA 168   0.9547 0.9128 0.0291% 0.1064 N N treatment na 

ve CGPLH486 Healthy WGS Preoperative NA 169   0.9199 0.9042 0.0220% 0.0820 N N treatment na 

ve CGPLH487 Healthy WGS Preoperative NA 169   0.9575 0.9098 0.0594% 0.2154 N N treatment na 

ve CGPLH488 Healthy WGS Preoperative NA 167   0.9618 0.9298 0.0409% 0.0903 N N treatment na 

ve CGPLH490 Healthy WGS Preoperative NA 167   0.8950 0.8794 0.0432% 0.0424 N N treatment na 

ve CGPLH491 Healthy WGS Preoperative NA 168   0.9631 0.9332 0.0144% 0.0223 N N treatment na 

ve CGPLH492 Healthy WGS Preoperative NA 170   0.9335 0.8799 0.0322% 0.0311 N N treatment na 

ve CGPLH493 Healthy WGS Preoperative NA 168   0.8718 0.9330 0.0065% 0.0280 N N treatment na 

ve CGPLH494 Healthy WGS Preoperative NA 169   0.9623 0.9303 0.0232% 0.0824 N N treatment na 

ve CGPLH495 Healthy WGS Preoperative NA 166   0.8777 0.8908 0.0513% 0.0465 N N treatment na 

ve CGPLH496 Healthy WGS Preoperative NA 166   0.8788 0.9398 0.0208% 0.0572 N N treatment na 

ve CGPLH497 Healthy WGS Preoperative NA 167   0.9576 0.9330 0.0335% 0.0404 N N treatment na 

ve CGPLH498 Healthy WGS Preoperative NA 167   0.9526 0.9315 0.0403% 0.0752 N N treatment na 

ve CGPLH499 Healthy WGS Preoperative NA 167   0.9733 0.9442 0.0198% 0.0149 N N treatment na 

ve CGPLH500 Healthy WGS Preoperative NA 168   0.9542 0.9240 0.0433% 0.0754 N N treatment na 

ve CGPLH501 Healthy WGS Preoperative NA 169   0.9526 0.9308 0.0300% 0.0159 N N treatment na 

ve CGPLH502 Healthy WGS Preoperative NA 167   0.9512 0.9200 0.0351% 0.0841 N N treatment na 

ve CGPLH503 Healthy WGS Preoperative NA 169   0.8947 0.8939 0.0398% 0.0649 N N treatment na 

ve CGPLH504 Healthy WGS Preoperative NA 167   0.9561 0.9324 0.0440% 0.1231 N N treatment na 

ve CGPLH505 Healthy WGS Preoperative NA 166   0.9554 0.9243 0.0605% 0.1869 N N treatment na 

ve CGPLH506 Healthy WGS Preoperative NA 167   0.9733 0.9498 0.0284% 0.0180 N N treatment na 

ve CGPLH507 Healthy WGS Preoperative NA 168   0.9222 0.9192 0.0186% 0.0848 N N treatment na 

ve CGPLH508 Healthy WGS Preoperative NA 167   0.9674 0.9410 0.0150% 0.1077 N N treatment na 

ve CGPLH509 Healthy WGS Preoperative NA 167   0.9475 0.9323 0.0163% 0.0828 N N treatment na 

ve CGPLH510 Healthy WGS Preoperative NA 167   0.9459 0.9548 0.0128% 0.0378 N N treatment na 

ve CGPLH511 Healthy WGS Preoperative NA 168   0.9714 0.9493 0.0224% 0.1779 N N treatment na 

ve CGPLH512 Healthy WGS Preoperative NA 168   0.9442 0.9244 0.0094% 0.0076 N N treatment na 

ve CGPLH513 Healthy WGS Preoperative NA 166   0.9705 0.9595 0.0441% 0.5250 N N treatment na 

ve CGPLH514 Healthy WGS Preoperative NA 167   0.9690 0.9369 0.0114% 0.3131 N N treatment na 

ve CGPLH515 Healthy WGS Preoperative NA 167   0.9568 0.9283 0.0352% 0.4936 N N treatment na 

ve CGPLH516 Healthy WGS Preoperative NA 168   0.9508 0.9298 0.0175% 0.0916 N N treatment na 

ve CGPLH517 Healthy WGS Preoperative NA 168   0.9635 0.9494 0.0161% 0.0059 N N treatment na 

ve CGPLH518 Healthy WGS Preoperative NA 168   0.9647 0.9432 0.0274% 0.0130 N N treatment na 

ve CGPLH519 Healthy WGS Preoperative NA 166   0.9366 0.9351 0.0171% 0.0949 N N treatment na 

ve CGPLH520 Healthy WGS Preoperative NA 166   0.9649 0.9476 0.0241% 0.0944 N N treatment na 

ve CGPLH625 Healthy WGS Preoperative NA 166   0.8766 0.9231 0.0697% 0.4977 N N treatment na 

ve CGPLH626 Healthy WGS Preoperative NA 170   0.9011 0.9269 0.0231% 0.3100 N N treatment na 

ve CGPLH639 Healthy WGS Preoperative NA 165   0.9482 0.9410 0.0549% 0.0773 N N treatment na 

ve CGPLH640 Healthy WGS Preoperative NA 166   0.9131 0.9264 0.0232% 0.0327 N N treatment na 

ve CGPLH642 Healthy WGS Preoperative NA 167   0.9641 0.9376 0.0768% 0.0555 N N treatment na 

ve CGPLH643 Healthy WGS Preoperative NA 169   0.9450 0.9271 0.0579% 0.1325 N N treatment na 

ve CGPLH644 Healthy WGS Preoperative NA 170   0.9398 0.8948 0.0621% 0.3819 N N treatment na 

ve CGPLH646 Healthy WGS Preoperative NA 172   0.9296 0.8691 0.0462% 0.2423 N N — treatment na 

ve CGPLLU144 Lung Targeted Mutation Preoperative II 164   0.8702 0.8681 0.0423% 0.9892 Y Y  5.10% Cancer Analysis and WGS treatment na 

ve CGPLLU161 Lung Targeted Mutation Preoperative II 165   0.9128 0.9187 0.0273% 0.9955 Y Y  0.20% Cancer Analysis and WGS treatment na 

ve CGPLLU162 Lung Targeted Mutation Preoperative II 165   0.7753 0.8836 0.1410% 0.9986 Y Y  0.22% Cancer Analysis and WGS treatment na 

ve CGPLLU163 Lung Targeted Mutation Preoperative II 166   0.4770 0.3033 0.0724% 0.9940 Y Y  0.21% Cancer Analysis and WGS treatment na 

ve CGPLLU168 Lung Targeted Mutation Preoperative I 163   0.9164 0.8842 0.0712% 0.9861 Y Y  0.07% Cancer Analysis and WGS treatment na 

ve CGPLLU169 Lung Targeted Mutation Preoperative I 163   0.9326 0.9189 0.0846% 0.9866 Y Y  0.13% Cancer Analysis and WGS treatment na 

ve CGPLLU176 Lung Targeted Mutation Preoperative I 168   0.9572 0.9081 0.0626% 0.8769 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLLU177 Lung Targeted Mutation Preoperative II 166   0.8472 0.6790 0.0564% 0.9924 Y Y  3.22% Cancer Analysis and WGS treatment na 

ve CGPLLU203 Lung Targeted Mutation Preoperative II 164   0.9119 0.8741 0.0568% 0.9178 Y Y  0.11% Cancer Analysis and WGS treatment na 

ve CGPLLU205 Lung Targeted Mutation Preoperative II 163   0.9518 0.9476 0.0495% 0.9877 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLLU207 Lung Targeted Mutation Preoperative II 166   0.9344 0.9379 0.0421% 0.9908 Y Y  0.32% Cancer Analysis and WGS treatment na 

ve CGPLLU208 Lung Targeted Mutation Preoperative II 164   0.9091 0.8942 0.0815% 0.9273 Y Y  1.33% Cancer Analysis and WGS treatment na 

ve CGPLOV11 Ovarian Targeted Mutation Preoperative IV 166   0.8902 0.8872 0.0469% 0.9343 Y Y  0.87% Cancer Analysis and WGS treatment na 

ve CGPLOV12 Ovarian Targeted Mutation Preoperative I 167   0.8779 0.8973 0.2767% 0.9764 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLOV13 Ovarian Targeted Mutation Preoperative IV 166   0.7560 0.9146 0.1017% 0.9690 Y Y  0.35% Cancer Analysis and WGS treatment na 

ve CGPLOV15 Ovarian Targeted Mutation Preoperative III 165   0.8585 0.8552 0.0876% 0.9945 Y Y  3.54% Cancer Analysis and WGS treatment na 

ve CGPLOV16 Ovarian Targeted Mutation Preoperative III 165   0.9052 0.9046 0.0400% 0.9983 Y Y  1.12% Cancer Analysis and WGS treatment na 

ve CGPLOV19 Ovarian Targeted Mutation Preoperative II 165   0.7854 0.7578 0.1089% 0.9989 Y Y 46.35% Cancer Analysis and WGS treatment na 

ve CGPLOV20 Ovarian Targeted Mutation Preoperative II 165   0.8711 0.9154 0.0581% 0.9749 Y Y  0.21% Cancer Analysis and WGS treatment na 

ve CGPLOV21 Ovarian Targeted Mutation Preoperative IV 167   0.8942 0.8889 0.0677% 0.9961 Y Y 14.36% Cancer Analysis and WGS treatment na 

ve CGPLOV22 Ovarian Targeted Mutation Preoperative III 164   0.8944 0.9355 0.0251% 0.9775 Y Y  0.49% Cancer Analysis and WGS treatment na 

ve CGPLOV23 Ovarian Targeted Mutation Preoperative I 169   0.8510 0.8850 0.1520% 0.9916 Y Y  1.39% Cancer Analysis and WGS treatment na 

ve CGPLOV24 Ovarian Targeted Mutation Preoperative I 166   0.9449 0.8995 0.0303% 0.9856 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLOV25 Ovarian Targeted Mutation Preoperative I 166   0.9590 0.9228 0.0141% 0.8544 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLOV26 Ovarian Targeted Mutation Preoperative I 161   0.8148 0.9351 0.0646% 0.9946 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLOV28 Ovarian Targeted Mutation Preoperative I 167   0.9635 0.9378 0.0647% 0.8160 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLOV31 Ovarian Targeted Mutation Preoperative III 167   0.9461 0.9293 0.1606% 0.9795 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLOV32 Ovarian Targeted Mutation Preoperative I 168   0.9582 0.9338 0.1351% 0.8609 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLOV37 Ovarian Targeted Mutation Preoperative I 170   0.9397 0.8831 0.0986% 0.9849 Y Y  0.29% Cancer Analysis and WGS treatment na 

ve CGPLOV38 Ovarian Targeted Mutation Preoperative I 166   0.5779 0.6502 0.0490% 0.9990 Y Y  4.89% Cancer Analysis and WGS treatment na 

ve CGPLOV40 Ovarian Targeted Mutation Preoperative IV 170   0.6097 0.8127 0.6145% 0.9983 Y Y  6.73% Cancer Analysis and WGS treatment na 

ve CGPLOV41 Ovarian Targeted Mutation Preoperative IV 167   0.9403 0.8929 0.1110% 0.9484 Y Y  0.60% Cancer Analysis and WGS treatment na 

ve CGPLOV42 Ovarian Targeted Mutation Preoperative I 166   0.9265 0.9086 0.0489% 0.9979 Y Y  1.24% Cancer Analysis and WGS treatment na 

ve CGPLOV43 Ovarian Targeted Mutation Preoperative I 167   0.9626 0.9342 0.0432% 0.6042 N N ND Cancer Analysis and WGS treatment na 

ve CGPLOV44 Ovarian Targeted Mutation Preoperative I 164   0.9536 0.9173 0.1946% 0.9962 Y Y  0.37% Cancer Analysis and WGS treatment na 

ve CGPLOV46 Ovarian Targeted Mutation Preoperative I 166   0.9622 0.9291 0.0801% 0.9128 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLOV47 Ovarian Targeted Mutation Preoperative I 165   0.9704 0.9461 0.0270% 0.3410 N N  3.20% Cancer Analysis and WGS treatment na 

ve CGPLOV48 Ovarian Targeted Mutation Preoperative I 167   0.9675 0.9429 0.0422% 0.4874 N N 10.70% Cancer Analysis and WGS treatment na 

ve CGPLOV49 Ovarian Targeted Mutation Preoperative III 164   0.8998 0.8083 0.1527% 0.9897 Y Y  2.03% Cancer Analysis and WGS treatment na 

ve CGPLOV50 Ovarian Targeted Mutation Preoperative III 165   0.9682 0.9382 0.0807% 0.9955 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLPA112 Pancreatic WGS Preoperative II 164   0.8914 0.9429 0.0268% 0.0856 N N Cancer treatment na 

ve CGPLPA113 Duodenal WGS Preoperative I 170   0.8751 0.7674 1.0116% 0.9935 Y Y Cancer treatment na 

ve CGPLPA114 Bile Duct WGS Preoperative II 166   0.9098 0.9246 0.0836% 0.7598 Y Y Cancer treatment na 

ve CGPLPA115 Bile Duct WGS Preoperative IV 165   0.8053 0.8310 0.0763% 0.9974 Y Y Cancer treatment na 

ve CGPLPA117 Bile Duct WGS Preoperative II 165   0.9395 0.8767 0.1084% 0.9049 Y Y — Cancer treatment na 

ve CGPLPA118 Bile Duct Targeted Mutation Preoperative I 167   0.9406 0.9001 0.1842% 0.9859 Y Y  0.14% Cancer Analysis and WGS treatment na 

ve CGPLPA122 Bile Duct Targeted Mutation Preoperative II 164   0.8231 0.8058 0.2047% 0.9983 Y Y 37.22% Cancer Analysis and WGS treatment na 

ve CGPLPA124 Bile Duct Targeted Mutation Preoperative II 166   0.9108 0.9238 0.1542% 0.8791 Y Y  0.62% Cancer Analysis and WGS treatment na 

ve CGPLPA125 Bile Duct WGS Preoperative II 166   0.9675 0.9373 0.0273% 0.0228 N N — Cancer treatment na 

ve CGPLPA126 Bile Duct Targeted Mutation Preoperative II 166   0.9155 0.9139 0.4349% 0.9908 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLPA127 Bile Duct WGS Preoperative IV 167   0.8916 0.8117 0.4371% 0.9789 Y Y — Cancer treatment na 

ve CGPLPA128 Bile Duct Targeted Mutation Preoperative II 167   0.9262 0.9003 0.1317% 0.9812 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLPA129 Bile Duct Targeted Mutation Preoperative II 166   0.9220 0.9155 0.0642% 0.9839 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLPA130 Bile Duct Targeted Mutation Preoperative II 169   0.8586 0.8499 0.1005% 0.9895 Y Y ND Cancer Analysis and WGS treatment na 

ve CGPLPA131 Bile Duct Targeted Mutation Preoperative II 165   0.7707 0.9195 0.0780% 0.9885 Y Y  0.21% Cancer Analysis and WGS treatment na 

ve CGPLPA134 Bile Duct Targeted Mutation Preoperative II 160   0.7502 0.8847 0.0260% 0.9896 Y Y  0.93% Cancer Analysis and WGS treatment na 

ve CGPLPA135 Bile Duct WGS Preoperative I 165   0.9495 0.9184 0.0558% 0.6594 Y N Cancer treatment na 

ve CGPLPA136 Bile Duct Targeted Mutation Preoperative II 164   0.9289 0.9050 0.0769% 0.9596 Y Y  0.10% Cancer Analysis and WGS treatment na 

ve CGPLPA137 Bile Duct WGS Preoperative II 166   0.9568 0.9320 0.0499% 0.7282 Y N Cancer treatment na 

ve CGPLPA139 Bile Duct WGS Preoperative IV 166   0.9511 0.9374 0.0465% 0.0743 N N Cancer treatment na 

ve CGPLPA14 Pancreatic WGS Preoperative II 167   0.8718 0.9069 0.0515% 0.9824 Y Y Cancer treatment na 

ve CGPLPA140 Bile Duct Targeted Mutation Preoperative II 166   0.9215 0.9548 0.0330% 0.9761 Y Y  0.21% Cancer Analysis and WGS treatment na 

ve CGPLPA141 Bile Duct WGS Preoperative II 165   0.9172 0.9381 0.0920% 0.9988 Y Y Cancer treatment na 

ve CGPLPA15 Pancreatic WGS Preoperative II 167   0.9111 0.8927 0.0160% 0.8737 Y Y Cancer treatment na 

ve CGPLPA155 Bile Duct WGS Preoperative II 165   0.9496 0.9313 0.0260% 0.8013 Y Y Cancer treatment na 

ve CGPLPA156 Pancreatic WGS Preoperative II 167   0.9479 0.9432 0.0290% 0.0159 N N Cancer treatment na 

ve CGPLPA165 Bile Duct WGS Preoperative I 168   0.9596 0.9309 0.0558% 0.2158 N N Cancer treatment na 

ve CGPLPA168 Bile Duct WGS Preoperative II 162   0.7838 0.7757 0.3123% 0.9878 Y Y Cancer treatment na 

ve CGPLPA17 Pancreatic WGS Preoperative II 166   0.8624 0.6771 1.2600% 0.9956 Y Y Cancer treatment na 

ve CGPLPA184 Bile Duct WGS Preoperative II 165   0.9100 0.9203 0.0897% 0.9926 Y Y Cancer treatment na 

ve CGPLPA187 Bile Duct WGS Preoperative II 165   0.8577 0.8968 0.0658% 0.9675 Y Y Cancer treatment na 

ve CGPLPA23 Pancreatic WGS Preoperative II 165   0.7887 0.6938 0.5785% 0.9984 Y Y Cancer treatment na 

ve CGPLPA25 Pancreatic WGS Preoperative II 166   0.9549 0.9239 0.0380% 0.8103 Y Y Cancer treatment na 

ve CGPLPA26 Pancreatic WGS Preoperative II 166   0.9598 0.9356 0.0247% 0.8231 Y Y Cancer treatment na 

ve CGPLPA28 Pancreatic WGS Preoperative II 165   0.9069 0.8938 0.0546% 0.9036 Y Y Cancer treatment na 

ve CGPLPA33 Pancreatic WGS Preoperative II 166   0.8361 0.8553 0.0894% 0.9967 Y Y Cancer treatment na 

ve CGPLPA34 Pancreatic WGS Preoperative II 168   0.8946 0.8885 0.0439% 0.7977 Y Y Cancer treatment na 

ve CGPLPA37 Pancreatic WGS Preoperative II 165   0.8840 0.9294 0.0410% 0.9924 Y Y Cancer treatment na 

ve CGPLPA38 Pancreatic WGS Preoperative II 167   0.8746 0.8941 0.0372% 0.9851 Y Y Cancer treatment na 

ve CGPLPA39 Pancreatic WGS Preoperative II 167   0.8562 0.7972 0.5058% 0.9951 Y Y Cancer treatment na 

ve CGPLPA40 Pancreatic WGS Preoperative II 165   0.8563 0.8865 0.2268% 0.9920 Y Y Cancer treatment na 

ve CGPLPA42 Pancreatic WGS Preoperative II 167   0.9126 0.8863 0.0283% 0.3544 N N Cancer treatment na 

ve CGPLPA46 Pancreatic WGS Preoperative II 169   0.8274 0.7525 1.0982% 0.9952 Y Y Cancer treatment na 

ve CGPLPA47 Pancreatic WGS Preoperative II 166   0.8376 0.8439 0.1596% 0.9946 Y Y Cancer treatment na 

ve CGPLPA48 Pancreatic WGS Preoperative I 167   0.9391 0.9207 1.0232% 0.2251 N N Cancer treatment na 

ve CGPLPA52 Pancreatic WGS Preoperative II 167   0.9452 0.8863 0.0154% 0.0963 N N Cancer treatment na 

ve CGPLPA53 Pancreatic WGS Preoperative I 163   0.9175 0.8776 0.1824% 0.8946 Y Y Cancer treatment na 

ve CGPLPA58 Pancreatic WGS Preoperative II 165   0.9587 0.9224 0.0803% 0.9054 Y Y Cancer treatment na 

ve CGPLPA59 Pancreatic WGS Preoperative II 163   0.9230 0.9193 0.1479% 0.9759 Y Y Cancer treatment na 

ve CGPLPA67 Pancreatic WGS Preoperative III 166   0.9574 0.9248 0.0329% 0.6714 Y N Cancer treatment na 

ve CGPLPA69 Pancreatic WGS Preoperative I 168   0.9172 0.8592 0.0459% 0.1245 N N Cancer treatment na 

ve CGPLPA71 Pancreatic WGS Preoperative II 167   0.9424 0.8888 0.0479% 0.0524 N N Cancer treatment na 

ve CGPLPA74 Pancreatic WGS Preoperative II 166   0.9688 0.9372 0.0292% 0.0108 N N Cancer treatment na 

ve CGPLPA76 Pancreatic WGS Preoperative II 163   0.9681 0.9441 0.0345% 0.0897 N N Cancer treatment na 

ve CGPLPA85 Pancreatic WGS Preoperative II 165   0.9137 0.9337 0.0363% 0.0508 N N Cancer treatment na 

ve CGPLPA86 Pancreatic WGS Preoperative II 165   0.8875 0.8042 0.7564% 0.9864 Y Y Cancer treatment na 

ve CGPLPA92 Pancreatic WGS Preoperative II 167   0.9389 0.9003 0.1458% 0.7061 Y N Cancer treatment na 

ve CGPLPA93 Pancreatic WGS Preoperative II 166   0.8585 0.8023 0.6250% 0.9978 Y Y Cancer treatment na 

ve CGPLPA94 Pancreatic WGS Preoperative II 162   0.9365 0.9433 0.0180% 0.9025 Y Y Cancer treatment na 

ve CGPLPA95 Pancreatic WGS Preoperative II 163   0.8542 0.8571 0.0815% 0.9941 Y Y — Cancer treatment na 

ve CGST102 Gastric Targeted Mutation Preoperative II 167   0.9496 0.9057 0.0704% 0.8581 Y Y  0.43% Cancer Analysis and WGS treatment na 

ve CGST11 Gastric WGS Preoperative IV 169   0.9419 0.9161 0.0651% 0.1435 N N — Cancer treatment na 

ve CGST110 Gastric Targeted Mutation Preoperative III 167   0.9626 0.9232 0.0817% 0.8900 Y Y ND Cancer Analysis and WGS treatment na 

ve CGST114 Gastric Targeted Mutation Preoperative III 164   0.9535 0.9038 0.0317% 0.5893 N N ND Cancer Analysis and WGS treatment na 

ve CGST13 Gastric Targeted Mutation Preoperative II 166   0.9369 0.9156 0.0321% 0.9754 Y Y ND Cancer Analysis and WGS treatment na 

ve CGST131 Gastric WGS Preoperative III 171   0.9428 0.8886 0.2752% 0.9409 Y Y — Cancer treatment na 

ve CGST141 Gastric Targeted Mutation Preoperative III 168   0.9621 0.9206 0.0388% 0.2008 N N ND Cancer Analysis and WGS treatment na 

ve CGST16 Gastric Targeted Mutation Preoperative III 166   0.7804 0.8355 0.1744% 0.9974 Y Y  0.93% Cancer Analysis and WGS treatment na 

ve CGST18 Gastric Targeted Mutation Preoperative II 169   0.9523 0.9111 0.0299% 0.3842 N N  0.14% Cancer Analysis and WGS treatment na 

ve CGST21 Gastric WGS Preoperative II 165 −0.4778 0.2687 0.2299% 0.9910 Y Y Cancer treatment na 

ve CGST26 Gastric WGS Preoperative IV 166   0.9554 0.9140 0.0399% 0.5009 N N — Cancer treatment na 

ve CGST28 Gastric Targeted Mutation Preoperative X 169   0.9076 0.7832 0.1295% 0.9955 Y Y 1.62% Cancer Analysis and WGS treatment na 

ve CGST30 Gastric Targeted Mutation Preoperative III 169   0.9246 0.9121 0.0338% 0.9183 Y Y 0.42% Cancer Analysis and WGS treatment na 

ve CGST32 Gastric Targeted Mutation Preoperative II 169   0.9431 0.8639 0.0247% 0.9612 Y Y 2.99% Cancer Analysis and WGS treatment na 

ve CGST33 Gastric Targeted Mutation Preoperative I 168   0.7999 0.7770 0.0799% 0.9805 Y Y 2.32% Cancer Analysis and WGS treatment na 

ve CGST38 Gastric WGS Preoperative 0 168   0.9368 0.8758 0.0640% 0.9416 Y Y — Cancer treatment na 

ve CGST39 Gastric Targeted Mutation Preoperative IV 164   0.8742 0.9401 0.0287% 0.8480 Y Y ND Cancer Analysis and WGS treatment na 

ve CGST41 Gastric Targeted Mutation Preoperative IV 168   0.8194 0.9284 0.0398% 0.9263 Y Y ND Cancer Analysis and WGS treatment na 

ve CGST45 Gastric Targeted Mutation Preoperative II 168   0.9576 0.9036 0.0220% 0.9713 Y Y ND Cancer Analysis and WGS treatment na 

ve CGST47 Gastric Targeted Mutation Preoperative I 168   0.9611 0.9096 0.0157% 0.9687 Y Y  0.45% Cancer Analysis and WGS treatment na 

ve CGST48 Gastric Targeted Mutation Preoperative IV 167   0.7469 0.5445 0.0220% 0.9975 Y Y  4.21% Cancer Analysis and WGS treatment na 

ve CGST53 Gastric WGS Preoperative 0 173 −0.0019 0.7888 0.1140% 0.9914 Y Y — Cancer treatment na 

ve — CGST58 Gastric Targeted Mutation Preoperative III 169   0.9470 0.9094 0.0696% 0.9705 Y Y ND Cancer Analysis and WGS treatment na 

ve CGST67 Gastric WGS Preoperative I 170   0.9352 0.8853 0.3245% 0.9032 Y Y Cancer treatment na 

ve CGST77 Gastric WGS Preoperative IV 170   0.0043 0.8295 0.1851% 0.9981 Y Y — Cancer treatment na 

ve CGST80 Gastric Targeted Mutation Preoperative III 168   0.9313 0.8846 0.0490% 0.9513 Y Y  1.04% Cancer Analysis and WGS treatment na 

ve CGST81 Gastric Targeted Mutation Preoperative I 168   0.9480 0.8851 0.0138% 0.9748 Y Y  0.20% Cancer Analysis and WGS treatment na 

ve *ND indicated not detected. Please see reference 10 for additional information on targeted sequence analyes. DELFI cancer detection at 95% and 98% specificity is based on scores greater than 0.6200 and 0.7500, respectively. 

What is claimed is:
 1. A method of treating a subject comprising: identifying a subject as having cancer by determining a cell free DNA (cfDNA) fragmentation profile of sequenced fragments in a sample obtained from the subject, wherein the sequenced fragments are obtained through whole genome sequencing (WGS); mapping the sequenced fragments to a genome to obtain windows of mapped sequences; analyzing the windows of mapped sequences to determine the cfDNA fragmentation profile; analyzing the cfDNA fragmentation profile against a reference cfDNA fragmentation profile from a healthy subject; wherein the cfDNA fragmentation profile comprises a ratio of small cfDNA fragments to large cfDNA fragments; detecting that the cfDNA fragmentation profile obtained from the subject is more variable than the reference cfDNA fragmentation profile, wherein increased variability of the fragmentation profile obtained from the subject is indicative of the subject as having cancer; and administering to the subject identified as having cancer, an immunotherapeutic treatment suitable for the treatment of cancer, thereby treating the subject.
 2. The method of claim 1, wherein the cancer is selected from the group consisting of colorectal cancer, lung cancer, breast cancer, gastric cancers, pancreatic cancers, bile duct cancers, and ovarian cancer.
 3. The method of claim 1, wherein the immunotherapeutic treatment is a treatment selected from the group consisting of immunotherapy, adoptive T cell therapy, targeted therapy, and combinations thereof.
 4. The method of claim 1, wherein the reference cfDNA fragmentation profile is generated by determining a cfDNA fragmentation profile in a sample obtained from the healthy subject.
 5. The method of claim 1, wherein the reference DNA fragmentation pattern is a reference nucleosome cfDNA fragmentation profile.
 6. The method of claim 1, wherein determining of the cfDNA fragmentation profile comprises determining a median fragment size, and wherein a median fragment size of the cfDNA fragmentation profile is shorter than a median fragment size of the reference cfDNA fragmentation profile.
 7. The method of claim 1, wherein determining of the cfDNA fragmentation profile comprises determining a fragment size distribution, and wherein a fragment size distribution of the cfDNA fragmentation profile differs by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile.
 8. The method of claim 1, wherein the cfDNA fragmentation profile comprises a ratio of small cfDNA fragments to large cfDNA fragments in windows of mapped sequences, wherein a small cfDNA fragment is about 100 base pairs (bp) to 150 bp in length, wherein a large cfDNA fragment is about 151 bp to 220 bp in length.
 9. The method of claim 1, wherein the cfDNA fragmentation profile comprises small cfDNA fragments in windows across the genome.
 10. The method of claim 1, wherein the cfDNA fragmentation profile comprises large cfDNA fragments in windows across the genome.
 11. The method of claim 1, wherein the cfDNA fragmentation profile comprises small and large cfDNA fragments in windows across the genome.
 12. The method of claim 1, wherein analyzing the cfDNA fragmentation profile against a reference cfDNA fragmentation profile from a healthy subject comprises analyzing the cfDNA fragmentation profile relative to a reference cfDNA fragmentation profile over a subgenomic interval.
 13. The method of claim 8, wherein genome coverage of the mapped sequences is from about 2×, 1×, 0.5×, 0.2× or 0.1×.
 14. The method of claim 1, wherein each window is from thousands to millions of bases in length.
 15. The method of claim 8, wherein the correlation of small to large fragment ratios in the cfDNA fragmentation profile for a subject with cancer as compared to the reference cfDNA fragmentation profile is lower than a correlation of small to large fragment ratios for a healthy subject as compared to the reference cfDNA fragmentation profile.
 16. The method of claim 1, wherein the sample is selected from the group consisting of blood, serum, plasma, amnion, tissue, urine, cerebrospinal fluid, saliva, sputum, broncho-alveolar lavage, bile, lymphatic fluid, cyst fluid, stool, ascites, pap smears, breast milk, and exhaled breath condensate.
 17. The method of claim 1, further comprising administering to the subject a treatment including surgery, chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy or a combination thereof.
 18. The method of claim 1, wherein a cell free DNA (cfDNA) fragmentation profile is also determined during and/or after administration of the treatment. 